Neck, shoulders, elbows, wrists, ankles, knees, who cares? It’s only back pain that must be addressed.

Having mostly sorted out the back, my chiro is now going searching for other aches and pains.

I said to her I’m like a ’64 Volkswagen Kombi; if you go looking for rust, you’ll find it. Eventually you’ll give up and buy a Hyundai.

According to GPT I need 3,000 calories a day.

And I would need to eat about 375 oysters a day to reach that 3,000 calories.

That’s about $1,000 a day at current pricing.

Just imagine, in the old days the indigenous locals at Jervis Bay did just that, for free.

For the record, according to GPT, I could push that to $27k a day by switching to Matsutake mushrooms.

I’m not chasing fame, fortune, power or legacy. I like moving through ideas, testing and breaking them down, then moving on. It’s about solving problems well. The process is the point, not the product.

Well that’s inconvenient isn’t it?

The latest US assassin was a right wingish Mormon steeped in online culture. MAGA-like that is.

“We are confident we have the right individual in custody,” an FBI spokesperson said, “but we are still working to establish the full picture of who he is and why he acted.”

I’m surprised they want to know why. And I’ll be even more surprised if they ever tell America – they’ll just fabricate whatever suits the narrative. Is the bloke even obliged to tell them why? Either he did it or he didn’t; that’s all they need to get a conviction.

Discord issued a statement saying his account had been suspended. “We have removed the suspect’s account for violating our off-platform behavior policy,” it said.

It’s nice to know they have some standards. Guns are a constitutional right, but memes are a privilege.

Maybe the US should extend the 2nd amendment rights to the access and use of social media.

Teenagers are effectively energy sinks. Their bodies are building adult frames. Their brains are rewiring as their frontal cortex comes online, a process that consumes about 20% of their excessive calorie intake. 

Growth spurts, muscle development, and hormonal surges all add to the demands. As does a diet of pure carbs.

Social grace in teenagers is rare because manners require emotional regulation and perspective, skills still developing in a brain dominated by the amygdala. Social separation from adults, exposure to social media and peer-driven culture make things worse.

So its best to give them the benefit of the doubt and just pretend it didn’t happen. After all, they will; they’re certified goldfish.

Gattaca, that sci-fi film, came out in 1997, when the new science of genetics was new and scary. The Human Genome Project was still underway, Dolly the sheep was a headline and the idea of designing a baby was treated as both a miracle and a threat.

The film imagined a future where  genetic analysis machines had the power to predict a person’s life expectancy from their DNA. Social structures were defined by DNA. Scary stuff.

Nearly thirty years on, we can edit genes, sequence entire genomes cheaply and we have a 10-year project to sequence every living thing, yet society hasn’t divided itself on the basis of DNA.

These days we worry about machine learning models ruling the world and making us all slaves. AI is today’s genetics, hyped as the next deadly threat to our position at the apex of the ecosystem.

Genetics hasn’t reshaped humanity’s day-to-day experience. AI probably won’t either, at least not in the way headlines suggest.

One thing that hasn’t changed; Michael Nyman’s music remains as annoying as ever.

-h-

Counter argument … I just walked into Coles to buy sausages for dinner. I took with me the espresso I’d just bought.

At the counter, their AI vision system determined that I had an item that I didn’t scan – the espresso cup and was therefore stealing.

The alarm went off. A human came over, reviewed the footage with coffee cup (the offending item) highlighted in green, then released me.

Hopefully by the time they dispense with the expensive human intervention, the machines have learnt from these errors.

It doesn’t have to know why I was released from custody, just that i had committed no crime.

Car window buttons. Just put a different physical texture on the front and rear buttons so the driver knows immediately from feel alone that they’ve selected the wrong set, or not.

In typography, the emdash was designed to match the width of a capital M, hence “emdash”.

There’s two others;

The en dash is half as wide, and

The hyphen is narrower still, a third of an emdash.

Keyboards don’t usually have options for en or em dashes because:

1. These were designed for typesetters, not casual writing, and

2. Most modern readers don’t notice or care about the distinction. Or they didn’t until GPT came along. Unfortunately the emdash is now a dead giveaway for AI generated content.

This Gboard keyboard that I’m using, to its credit, has the option. It only took me 20 minutes to find it. All you have to do is go to the number and symbol page, find the hyphen, long press it, then select the actual dash/hyphen that you want from the popup bubble. Simple as…

I’m thinking to add some emdashes to my own prose just to confuse people.

A group that has survived genocide possesses direct knowledge of its harm. When that group chooses to inflict genocide on others they act with full awareness of the suffering they are reproducing.

The crime is not only the destruction of lives and culture but also the conscious disregard of the insight gained through their own history.

In my mind, this makes the act ethically worse than genocide committed in a state of ignorance or stupidity, because it combines atrocity with a conscious rejection of empathy and memory.

You can liken it to comparing a crime of passion killing and a premeditated murder.

Ethical responsibility may not pass automatically through ancestry, but invoking history as ethical currency has consequences. If a state demands recognition and security on the basis of past suffering, it cannot expect impunity when its own actions mirror the crimes it condemns.

It seems Viv has inherited an intelligence that will be deeply unappreciated by the education system.

This morning’s discussion with Viv began with prime numbers, and ended with a demolition of several centuries of mathematics and philosophy.

I told him that some numbers cannot be divided by anything but themselves and one (to give an integer), and that no one really knows why primes behave the way they do.

He listened carefully, asked a few practical questions, and then suggested that the whole thing is pointless; numbers aren’t real, humans invented them, and that’s why they don’t make sense.

It is difficult to argue with Occam’s razor when deployed by a seven-year-old. It appears he has not yet been trained to mistake complexity for reality.

To him, “three” is only a word we use to describe a set of fingers, or biscuits. If the language is inconsistent, that is not evidence of cosmic mystery but a flaw in the design.

We like to pretend mathematics was discovered, not invented, because it flatters us to imagine we have glimpsed the machinery of the universe.

Viv is not impressed. He sees arithmetic as a tool, no more mysterious than a hammer, and if a tool misbehaves, you either fix it or chuck it.

That outlook will probably last until they teach him algebra, where the machinery becomes baroque enough to fool most sceptics, by design.

For now, he is satisfied with the insight that numbers are stories humans tell about counting, and that some of the stories are stranger than others.

I am satisfied too: there is no better reminder of how thin the line is between knowledge and faith than a child refusing to accept that 3, 5, and 7 are mysterious but 9 isn’t.

-h-

I like to think there’s an alternative quantum-like number system lurking out there. Noting that quantum physics and chemistry both collapse to classical theory in the limiting cases, why not maths as well?

I think it’s stupid that we use classical maths to describe quantum theory. It’s shoving a very square peg into a round hole, and no wonder it’s hard to follow.

Just as quantum mechanics reduces to classical mechanics for large quantum numbers, this hypothetical maths would reduce to ordinary arithmetic and calculus in the macroscopic limit.

Maybe primes only seem mysterious because they’re artifacts of the framework we use; features of our chosen mathematical coordinates rather than of reality itself. In a deeper structure, they might be irrelevant or invisible.

Or, as Viv says, numbers may just be projections of human cognition, a linguistic framework imposed on reality, rather than features of the universe itself.

So this morning I explained prime numbers to Viv;

Me: all numbers can be divided by one and themselves, right?

Viv: yes

Me: but some numbers can’t be divided by any other number, and nobody knows why.

Viv: that’s not true. You can divide any number by 2.

Me: oh, I forgot to say, the product must be a whole number. No fractions.

Viv: oh, so it’s every odd number?

Me: try 9

Viv: oh yeah … wait, what?

Me: no one knows why.

Viv: I think its because humans made up numbers and they don’t really mean anything.

Soup, man.

The fuckers enticed me to “trial” Gemini for free. A chance to see how Google’s latest and greatest stacks up against the competition.

It turns out it’s rubbish compared to GPT for what I do. And slow, to boot.

No problem, I thought. I’ll just cancel the trial. That’s when I discovered it wasn’t a free trial anymore. I’m paying for this shit.

My simple task had become a mission: get my old plan back and stop paying for a tool I don’t want.
And that’s where the ethical line got crossed. I went to “downgrade” to my previous plan, and there’s no option anywhere. None. Just a comprehensive list of more expensive plans that I can upgrade to.

Good job, Google; we’ll turn a blind eye to the monopoly so long as you pretend to be a good corporate citizen. Walling off the exit isn’t that.

So I asked Gemini to help me cancel Gemini. It couldn’t. To its credit, it did acknowledge the absurdity of the situation, noting it was a “sharp, insightful, and frankly damning summary” when I pointed out the deep irony of the situation.

Gemini’s final suggestion? Email customer support.

I did. An email came back from “JC” at the Google One team. It was a masterpiece of AI-generated empathy, a word-perfect template that managed to say nothing while asking me to do the very thing I had already told them was impossible.

“Thank you for contacting Google One support and for being a Google One member!

This is JC and I’ll be the one who will assist you today. I hope this email finds you well!

I do apologize for the delayed response.

I understand that you want to downgrade from the Google One 2TB Premium plan to the original 2TB plan because you do not want to pay for the Gemini AI feature. I know how important this is for you to resolve as soon as possible. No worries, let’s work on this together.

To better assist you and help resolve the issue as quickly as possible, could you please provide a few more details? This will help us investigate and give you accurate guidance:

When you go downgrading the subscription, can you please send me a screenshot of the option you have when you try to downgrade your subscription?
I’ll be looking forward to your response.”

So I went back to my unwanted AI assistant and complained that its advice had led me to another AI. Probably the same one through some internal API. And, no shit, this is what it suggested I do:

‘Reply directly to the email and write: “This automated response has not resolved my issue. The self-service options have failed. I demand this ticket be escalated to a human support agent for a manual plan downgrade.’”

Let that sink in. My unwanted and expensive Google AI assistant suggested that I get rid of it by zig-zagging around Google’s AI-generated customer support with Google’s AI-generated prompt engineering.

This snake has gone so far up its own arse that there’s only a black hole where it used to be.

The one thing I will say for Gemini is that you can copy any part of the LLM reply without having to hit the “select text” menu option in Android. It’s a small thing, but it goes to show that even with a groundbreaking technology like an LLM, good UI is 90% of the competitive differentiator.

On that subject, whoever first figures out how to make their LLM remember and inject user preferences into every single chat, they win. Down with emdashes!

Also I wouldn’t mind a “post chat to blog” option. Imagine all the great content that would be published and then used as the source of future LLM-generated answers?

A fully charged battery is heavier than a discharged battery, but not because it has gained any material.

A lithium-ion cell, like those used in a Tesla, is a sealed system: the same lithium, cobalt, nickel, graphite, and electrolyte remain inside whether it is empty or full. 

When you charge the battery, you are forcing lithium ions to leave the metal oxide cathode and insert themselves into graphite layers at the anode.

This rearrangement of matter stores energy in the chemical structure of the molecules, and energy has mass according to Einstein’s equation.

A Tesla Model 3 pack stores about 60 kWh, or roughly 216 megajoules, of energy. Dividing this by the square of the speed of light yields a mass increase of around 2.4 micrograms when it charges from empty.

That is one-twentieth the weight of a typical grain of salt, spread across a 480 kg battery pack.

The weight increase comes entirely from energy stored as chemical rearrangements, not from accumulating extra electrons. The electrons are just carriers; they help rearrange lithium ions and bonds inside the battery. Electrons circulate and are balanced by ionic motion; no net electron rest mass is added.

They didn’t teach me this at uni but every molecule is lighter than the sum of its isolated atoms, and every chemical reaction changes the total mass slightly. We ignore it in chemistry because the changes are at least a billion times smaller than atomic masses.

So much for the periodic table!

The molecules that make up all living things on Earth weigh roughly a few hundred thousand kilograms less than the same nuclei and electrons were they to be taken apart and separated to infinity. The exact number depends on composition and bonding, but it is in the 100-1000 tonne range (says GPT).

Viv says…

In 1933, this scientist started a robot that had, like, millions of specks of sound, and made them into a robot. After 9 years the first drop of sound appeared. Only 9 drops have ever fallen out.

He is referring to the pitch drop experiment at UQ. Pitch, sound…

“The slowest dropping thing is the highly viscous liquid in the pitch drop experiment by the University of Queensland (UQ). At room temperature, this substance, which is a form of tar or bitumen, flows so slowly that it can take years for a single drop to fall, with only nine drops having fallen since the experiment began in 1930.”

“The royal commission described robodebt as a ‘crude and cruel mechanism, neither fair nor legal’. It found that ‘people were traumatised on the off chance they might owe money’ and that robodebt was ‘a costly failure of public administration, in both human and economic terms”.

Could say the same about R&D tax.

Over the last few few years I’ve been collecting good candidate names for a band. The list so far;

Coda pi
Quiet carnage
Birdy Numnuts
half a mandible
glutinous pussies
Pine mullets
Rushman Saldie
No 4 fish puzzle
Wrong Heads
Quack and Dandy
Loopy VJs
The worms
Ado mato
The shelbows
The shobos
Reformed utopian army
The deagon deviation
Dodecarator
The cunning je suis
The avant gardener
The swamp bogans
Villainous Scroll
Emergency burgery
The S bend band
Moëdely
Sunken Seed
The Weilding Berkers

Space age chickens

Stone age chickens

The Malthusian trap describes a pre-industrial society where any small gains in productivity fueled population growth rather than higher living standards.

TL:DR – more people got to live in shittier conditions

Slave economies fed into this effect by tying wealth directly to ownership of people, turning human bodies into the primary unit of both production and capital.

Productivity scaled through reproduction and acquisition of slaves rather than mechanisation  More workers meant more output but also greater subsistence costs.

Slavery acted as a structural brake on innovation and a mechanism for keeping societies inside the Malthusian Trap.

As an institution, slavery had to go, for economic reasons.

Traditional Chinese Medicine makes up about thirty percent of China’s medical economy. Apparently not a single TCM herb had ever been shown in a clinical trial to be the best medicine for treating any condition. The only clear “best available medicine” that is derived from TCM is the pharmaceutically-purified compound class of artemisinin-based combination therapies (ACTs) for malaria. Everything else has, at best, adjunct benefits or is simply a placebo.

This is not a failure of science so much as a signature of a culture that never had its Enlightenment. Rationalism, falsifiability, and sceptical inquiry never became embedded. As a result, TCM is protected as cultural heritage, so it survives by decree and habit, rather than due to evidence.

In China, the machinery of science runs at full speed for vaccines, semiconductor fabrication and other sectors, but it is solely a tool applied where useful. They use science but they do not create it

In China’s culture, incumbency, fluency and authority are taken as proof of intelligence. Without deep norms of critique or falsifiability, an LLM already meets the bar for “general intelligence,” reflecting the local human standard of intelligence; one that leads to one-third of its medical budget being expended on products that have no proven efficacy.

That last blog got me thinking. My blog is unreviewed and not critiqued, so there’s no feedback loop. There’s no science in it, just words. So I can say what I want and get away with it.

So I got GPT to grade my blog. It assigned every recent post a score from zero to ten: zero for word soup, ten for formal reasoning. The average was 4.8 with a spread of 1.1. It might occasionally look rational and/or scientific, but not so.

It is a paradox. A machine trained to imitate judgement is now judging writing it did not create but could have (it says).

The exercise says more than the scores do. Without independent critique, both humans and machines live in a loop of confident language. It reads well, but it achieves nothing.

Then I got GPT to score my writing style. Each recent post got a number from zero to ten: zero for synthetic word soup, ten for strong human prose. The average was 5.0 with a standard deviation of 1.2.

Says GPT – “Most posts sit in the middle. A few are well-structured essays, some are fragments, and most are clear but unambitious.”

So according to GPT my thinking is half neuron soup and my writing is half word soup. I’m not sure whether to be happy or sad; a good score from GPT would have been the kiss of death, whereas a really shit score might be reflective of some hidden truth.

I’ve often wondered if writing this blog is worth it when it feels like publishing within a black hole; the problem being the absence of feedback from post-Enlightenment minds that aren’t tied to some convention or other.

What I’m really doing is refining my own thinking in a private lab-notebook style, with no feedback loop but a strong internal framework.

If you look closely at how large language models work you see an uncomfortable reflection of how most humans work.

LLMs are trained on huge quantities of written words, compressed into patterns. They use this data to guess the next word. I suspect that is not far from what most human brains do.

Billions of neurons form a web of associations, strengthened by repetition and context, until the act of thinking is mostly prediction. The familiarity of an LLM’s mistakes, the hallucinations, the confident nonsense, all supports this assertion.

Way back when, language turned memory into an external system, a way to stabilise and share mental models. Culture was built out of words and stories, but it did not make humans more rational; it simply socialised the biases.

Rational thinking and the scientific method was a later addition; a set of procedures designed to distrust intuition; state a hypothesis, test it, measure outcomes, critique and replicate results.

It broke the closed loop of pattern recognition; it is a tool we invented to work around a brain optimised for survival, not truth. In fact it’s invention marked the transition from survival to exponential growth on the exploitation of the environment.

LLMs are a mirror of the lower cognitive stack: a predictive system with vast memory but no metacognitive layer.

They mimic intelligence convincingly because much of what we call intelligence is just pattern prediction at scale. But they do not critique themselves or perform experiments, they do not doubt themselves, nor do they measure or care about success.

Without a scientific layer, they are locked in the same intuitive world humans occupied before the Enlightenment. But they are much faster.

So it’s the next step in AI development that will be the most revolutionary. When we encode rational thinking and the scientific method into the machines.

This will not come from bigger models but from programming machines to work like humans do when they’re doing rational thinking or science, layering a process of hypothesis, testing, critique and correction on top of raw prediction and heuristics.

The scientific method was humanity’s hack for escaping intuition, a framework that let us test truth rather than assume it. Embedding that same discipline into AI would create systems that do not just sound right but can be innovative.

The path to real machine reasoning could start small, with narrow domains and simple protocols rather than grand ambitions. A minimal system could focus on a tightly defined field like logic puzzles, geometry puzzles or formal proofs, using just a handful of agents: one to propose arguments, one to critique them, and one to verify each step against explicit rules.

Once it works, then we scale it.

In the meantime, the more useful LLMs are to you,  the more you’re living in the pre-Enlightenment mental space, like most people.

Precis; two cops shot dead by a sovereign citizen, Mr Dezi Freeman, when they went to his place to hassle him for something or other.

“The shooting – which appears very similar to an ambush of police in Queensland three years ago – has revived questions over how the country deals with growing sects of anti-government conspiracy theorists.”

(it’s not rocket science; leave them alone).

Sovereign citizens are anti-authoritarian conspiracists and pseudo-law believers: people who reject established law as illegitimate, using legal-sounding arguments that have no actual basis according to legal experts (but they would say that).

In practice, this results in behaviours ranging from refusing to register a car or holding a driver’s license, to – in the case of Mr Freeman – trying to use their own asserted authority to arrest a magistrate in court.

You see where he went wrong, right there. Hell hath no fury like a judge scorned.

Generally judges are deeply insecure nerds that have clawed their way to some level of ascendancy through the abuse they are free to hand out in court.

So, my suggestion, let’s ensure judges and magistrates are generally held to the same level of accountability regarding their behaviour as the rest of society. That might help.

This (the subject matter under discussion) is a screenshot of a news feed from The Guardian. It shows a headline about Moira Deeming, a Victorian politician, being criticised for describing the police as Daniel Andrews’ (the former Victorian Premier’s) “personal army.” The controversy arose during a debate about a statue, which happened while police were also conducting a manhunt for a suspect accused of killing two officers.

The criticism seems to come from the fact that her comments were seen as poorly timed or inflammatory, given the seriousness of the police operation underway.

Do you want me to dig deeper and check exactly what the statue debate was about, and how it linked to her comment?

—

Fuck no … what statue?

—

Jeff Kennett introduced a convention that any premier who serves over 3,000 days in office will be honoured with a statue outside 1 Treasury Place in Melbourne. Good on ya, Jeff.

Odd number, 3,000 days. That’s just over 8 years. Apparently he did it in days to account for leap years, or somesuch.

Dan Andrews served over 3,000 days but he’s widely hated because he went all communist dictator during COVID.

I suggest they put up the statue and then deface it, just so.

The specialist says my snoring is caused by a two-factor collapse of the soft palate, both dimensions being genetic.

The only effective management is to use a splint, and it has to be a proper, fitted splint AND I’d have to have palette surgery but the recovery is apparently awful, so he recommends leaving it unless things get much worse. At the moment my sleep data suggests it isn’t too bad.

He also advised not to gain weight. I don’t intend to, but he noted there’s a hysteresis effect: fat in the palate is the last to come off when losing weight, so snoring doesn’t necessarily improve in step with weight loss.

Unfortunately I’m in the bracket of snorers for whom there’s no simple fix. He called it the high hanging fruit, funny fucker.

Recovered carbon black (rCB) comes from the idea that if tyres are 20–30 per cent carbon black, you can cook them in a pyrolysis unit and be left with usable recycled carbon black filler.

But the product isn’t the same as virgin furnace carbon black. The recycled powder carries ash from zinc, silica and sulphur, and its surface chemistry has been baked and altered by years of service and by the thermal process itself.

The result is a weaker, inconsistent material that can only be blended at modest percentages into new tyres without hurting performance.

Each trip through the loop makes it worse. The first recovery yields something marginally useful, the second gives a filler fit for low-spec rubber, and by the third it is only good for road asphalt.

It is the same problem that paper pulp has: each recycling cycle shortens the fibres until they can no longer make paper. With rCB, each cycle adds ash and strips surface activity until it no longer behaves like the engineered material that was useful in tyres in the first place.

The carbon maths don’t help the story. Virgin carbon black costs about three kilograms of CO₂ per kilogram to make. Each pyrolysis recycle pass adds another 1.5–2 kilograms.

Run the material through three recoveries and you have emitted more than eight kilograms of CO₂ per kilogram of rCB, and its a valueless filler.

And that doesn’t include the frictional energy losses caused by having shitter tyres.

What is sold as a circular solution is really downcycling with a heavy energy and CO2 bill, a way of keeping waste tyres on the environmental balance sheet rather than writing them off and taking the hit.

Early car tyres at the start of the 20th century were made from natural rubber, which was light-coloured and wore out quickly. They were prone to cracking, abrasion, and heat damage. Around 1904, tyre makers began adding carbon black, originally soot from burning oil.

A small percentage of carbon black mixed into the rubber compound has the following benefits:

Strengthens the tyre by reinforcing the rubber matrix, greatly improving resistance to wear and abrasion, and improving traction.

Protects against UV degradation, since the black pigment absorbed and dissipated light that would otherwise break down the polymer chains.

Improves heat conduction, allowing tyres to shed heat more efficiently at high speeds, so grip stayed more consistent at higher speeds.

By the 1920s, nearly all major tyre manufacturers had adopted carbon black as a standard filler. Its use extended tyre life by a factor of five or more. Before, tyres were white or light-grey; after carbon black, black tyres became the norm.

Carbon black production grew into a large industry with purpose-built “furnace black” processes developed in the 1930s to produce consistent, high-surface-area material tailored for rubber reinforcement. Today, the majority of carbon black produced worldwide (around 70%) still goes into tyres, with the rest used in belts, hoses, plastics, inks, and coatings.

The composition of modern tyres is roughly as follows:

• Carbon black: ~22–28%
• Natural and/or synthetic rubber: ~40–50%
• Steel + textiles: ~10–15%
• Other additives (silica, zinc oxide, sulphur, oils, resins, etc.): ~10–15%
  

Recovered carbon black (rCB) has been around since the 1970s. It is the carbon black fraction left when the rubber in waste tyres is processed by pyrolysis or other controlled thermal treatments. Unlike open burning, pyrolysis is done in an oxygen-limited environment, so the rubber polymers crack into gases and oils without combusting. Steel is separated magnetically, oils can be refined for fuels or chemical feedstocks, and the residue is a fine, black powder that resembles, but isn’t identical to, virgin furnace carbon black.

The challenge with rCB is its quality and consistency. Virgin carbon black is manufactured with precise control of particle size, surface area, and structure, which directly determine a tyre’s wear, rolling resistance, and traction. rCB, on the other hand, carries remnants of ash (from other fillers in tyres like silica, zinc oxide, sulphur, etc.) and its surface chemistry has been altered by both the tyre’s service life and the pyrolysis process. As a result, it typically has lower value than fresh material. Most manufacturers who use rCB blend it at modest percentages (often around 10% of the carbon black used, so 2% of the total mass of a tyre) with virgin carbon black in new tyre compounds, balancing performance with sustainability goals.

On the positive side, rCB has become attractive because it closes the loop on one of the largest waste streams in the world. Over a billion tyres are scrapped annually. rCB use reduces landfill, cuts demand for fossil-derived feedstocks, but it increases CO₂ emissions compared to virgin production. Companies such as Michelin, Continental, Bridgestone and Goodyear have announced pilot or commercial use of rCB in selected product lines. At the same time, specialised recyclers (e.g. Scandinavian Enviro Systems, Pyrum Innovations, Delta-Energy) are scaling up industrial rCB production with more refined purification and activation techniques to get closer to virgin performance.

Like most things these days, it’s mostly bullshit. The energy used to recover the carbon black and the CO2 pollution created, means that tyres with rCB have a higher carbon footprint than normal tyres. It also degrades tyre performance. So they blend as little as they can in to justify the environmental marketing campaign.

Arguably it’s a good thing to keep tyres out of landfill but landfill is not that big of a problem. True, some countries allow the burning of old tyres, but they’re also the sort of countries that are never going to pay extra to use recycled materials.

In our lovely first world we can happily bury our old car tyres. Come back in few million years and the buried rubber will have converted to oil, which they can then burn to make their own carbon black. Now that’s what I call a circular economy.

Actual Vivvy quotes…

“When I grow up I don’t want to be an inventor. Life is pretty perfect already, and we’ve got enough silver and black stuff.”

Talking about biting the hand that feeds … it’s literally an obituary for the patent industry.

“I came up with a live (sic) hack. If you go slower, time goes faster.”

Physics re-written while waiting in line. Slow down, and somehow the day is done sooner. Relativity as reimagined in a tuckshop queue.

What strikes me as extraordinary is that at an age when most philosophy is still “why do I have to go to bed,” Viv has already discarded the necessity of invention and inverted the meaning of time. That feels profound because adults have built entire lives on the opposite assumptions.

My mate the GPT tells me that Dani Klein’s voice is unique. Her timbre is essentially a contralto blues voice filtered through chanson sensibility. There’s more but it was starting to bore me with detail. For example…

Good — let’s get anatomical. Dani Klein’s singing timbre comes from a few physiological traits and how she uses them.

—

1. Vocal fold closure and airflow

Her voice has a slightly breathy onset, meaning the vocal folds don’t snap fully shut at the start of a phrase. This lets a touch of air escape, giving the sound a husky, smoky edge.

But she balances that with firm closure once sustained, which prevents it from becoming weak or leaky. That’s why her husk sounds controlled rather than airy.


—

2. Chest-dominant resonance

Klein sings low in her range, leaning heavily on chest resonance (vibrations amplified in the thoracic cavity).

This gives the tone warmth and depth — the “contralto” quality. Most female pop/jazz singers are more head-dominant, hence lighter and brighter.


—

3. Pharyngeal shaping

She widens her pharynx (the throat cavity) and keeps her larynx relatively low when singing, creating a darkened timbre.

This produces the velvety quality — like a clarinet compared to a flute. It makes her sound “older” and “bluesier” than her actual physiology alone would.


—

4. Limited vibrato, straight tone

Vibrato comes from oscillation of vocal fold tension. Klein often suppresses that, holding a straight tone with just subtle micro-shades of vibrato.

Straight tone feels raw, conversational, and emotionally direct, while still under control. That restraint is part of her signature.


—

5. Micro-distortion: the grain

Occasionally, she uses slight irregular closure of the vocal folds — not enough to damage the voice, but enough to introduce a rasp.

That’s what gives her voice its sandpapery texture, somewhere between a jazz singer’s smooth tone and a blues singer’s growl.


—

6. Resonance balance

She blends chest resonance (depth) with just enough nasal and head resonance to carry the sound.

Without that, a low contralto can get muddy. Klein avoids that by letting the sound ping just a little in the mask (sinus/nasal cavities), keeping clarity while staying dark.


—

In short:
Physiologically, her voice is a low, chest-dominant contralto with controlled breathiness, pharyngeal darkening, minimal vibrato, and a deliberate touch of rasp from irregular fold closure. The result is that smoky, intimate, yet powerful timbre people associate with her.

—

Do you want me to map this to specific songs — like “What’s a Woman” or “Nah Neh Nah” — and point out exactly where those traits jump out in her singing?

With respect to the logic of taking vitamins, most people carry the fuzzy folk version not the Enlightenment lab version. The first is intuitive, the second is accurate, and in daily life the first usually wins.

The folk model goes like this: vitamins are little boosts. Vitamin C wards off colds. B-vitamins give you energy. Antioxidants clean you out. It is a simple story, requiring no measurement, and it’s easy to sell. The body is treated as a container that needs topping up, or as a clogged pipe that needs flushing.

The mechanistic model is less idiot friendly. Vitamins are co-factors in enzyme reactions. They act as agents that allow proteins to function. Vitamin C is needed to hydroxylate collagen; if you don’t have it, you get scurvy. Vitamin D regulates calcium absorption; without it, bones soften. B1, B3, B6 are parts of metabolic enzymes; without them, you get beriberi, pellagra and seizures. However, once the deficiency is corrected, taking more does absolutely nothing. If you don’t have the deficiency you are wasting your time and money.

Between those two models lies the gap where advertising feeds the bullshit. People prefer boost immunity because they don’t want to hear prevents scurvy. In fact, since they usually don’t have scurvy, that wouldn’t work.

In pre-Enlightenment imperial courts, eunuchs were employed precisely because they could not father children, a fact confirmed by centuries of observation. Yet at the slightest hint of sexual contact with their wards, they were executed to remove any doubt about royal lineage.

Today people swallow megadoses of vitamin C to fight colds, despite trial after trial showing no benefit.

In both cases, certainty is available but not wanted. A simple, fuzzy story is preferred, no matter what the cost.

The R&D Tax Incentive. First, you have to be a company, registered and eligible. Then you register for R&D tax. Then you register your annual R&D activities via their online form. Then you register again with the ATO when you file your tax return. So the process feels like register, then register again, then register once more. Each stage has the formality of “approval”, but none of them are.

The key is your self-assessment of your R&D: did you have a hypothesis, did you run experiments, do your records show it? If they review your self-assessment, AusIndustry decides whether your “R&D” was actually scientific research according to the Frascati Manual, or just ordinary product development dressed up in fancy words. The ATO checks whether the dollars you claimed really trace back to those activities.

I am not sure how your local SME is supposed to be an expert at the ridiculously obscure Frascati Manual bullshit approach to R&D. In truth, it’s anti-product development, so I am not sure these are skills we want to infect the broader business sector with. But wait, there’s R&D tax agents out there, so you can claim the R&D tax incentive without having to be contaminated with the scientific hypothesis.

R&D tax agents thrive on the gap between perception and reality. They will happily let you believe that all the thinking goes into the application, that once lodged it is somehow approved. They call it an audit if AusIndustry comes back, because “audit” is a familiar word, and less likely to make you question the premise. In truth, you have only registered your own self-assessment, and if the evil twins decide to “review” it, the burden of proof sits entirely on you.

The R&D tax agent takes your money for offering a few emails worth of advice and to cut and paste your self-assessment into the online form. What you are really paying for is the opportunity not to learn anything about the OECD Frascati Manual. Of course if you do get reviewed it doesn’t hurt the R&D tax agent at all. The risk is all yours and they get more money off you for managing the review process. If you lose, it doesn’t hurt their business at all; none of their other customers get to hear about it.

Tens of thousands of claims go through without being checked, while a few are “reviewed”. Of course the ATO doesn’t reveal the process for deciding which applications get reviewed, because that would let us design our self-assessments to avoid review. But my mate at the GPT reckons he knows.

He says; “The R&D Tax Incentive runs on self-assessment, but reviews are triggered through risk-based targeting rather than random audits. The ATO has said it uses “sophisticated systems” (read LLM) to detect non-compliance and specifically monitors industries of concern such as agriculture, construction, mining, and software development, as well as behaviours like claiming business-as-usual costs or poor record-keeping. AusIndustry has also shifted to a stricter compliance model, replacing informal education with formal examinations. In short, most claims go through untouched, but those that look unusual, aggressive, or fall into flagged sectors are far more likely to be examined.”

It is possible that the R&D tax agents pay for “drinks and dinner” (or near similar) with senior ATO folk in order to get the low-down on the review process. So maybe this also is what you are paying for when you pay them and then you do all the work.

But that’s Australia for you. Seems like 90% of the population earns a living by guiding the rest around the artificial barriers put up in their collective best interests. And the government carries on about our lack of “productivity”…

Simple maths: against the $4 billion headline cost of the R&D Tax Incentive you need to strip out the layers of leakage: government administration through the ATO and AusIndustry chews up perhaps 5–10% ($200–400 m); R&D tax agents and consultants take 10–20% of claims as fees ($400–800 m); companies themselves burn another 10–20% in staff time and compliance overhead ($400–800 m); the the R&D tax loans sector burns another $500m in profits extracted, and there’s the distortion cost of pseudo-research done only to satisfy the Frascati template, another $400–800 m with zero commercial yield. Add the opportunity cost of foregone product work and the fact that medium and large firms only get a non-refundable offset that is effectively repaid in future profitable years, and the $4 billion shrinks to zero gain, so we have negative productivity. That is, $4b of taxpayers money annually pissed up against the wall in the interest of a marketing campaign that no one cares about. Good job, Canberra!

Our local primary school’s drop’n’go zone is eerily similar to the pits of F1, without the driving skills. None, zero, zip.

One lane is the pit apron where the cars pull in, doors fling open, kids eject, bags tumble, instructions are barked and then the parents lurch back into the driving lane, driving as fast as their 4WD’s, SUV’s and Teslas allow. I assume they are all running late for work.

Unlike F1, there’s no crew, no stewards, no helmets and no stopwatch, just the vague but delusional hope that nobody gets clipped when they pull out into the driving lane.

Helpfully they’ve also added a pedestrian crossing right in the middle, just to spice things up.

That’d be an interesting addition for Lando, Lewis and Charles. Not Max though, he wouldn’t pause for pedestrians, not even little kiddies.

My latest fave headline…

“Stone Age Farmers Settled Near Dortmund Airport 7,000 Years Ago”

NSW police reckon they’ve spent over $10m on pro-Palestinean matters of late.

I think they are saying that’s a bad thing because while they were doing all that they weren’t out chasing villains.

Not yet, anyway, has anyone suggested they send the bill to Israel. 

They can’t send it Palestine because it doesn’t exist.

The primary beneficiary of a life short of meaning is the hot dog guy.

There really are only two types of people; those that like brown towels and those that don’t.

Semantic fidelity loss is related to hallucination, bias, synonym smoothing, and context drift. All of them have the same root cause; input text is mapped into vector space and then mapped back into language. This isn’t how we do it, so the differences in approaches express themselves as errors in our minds.

Language is already a second-order encoding: a representation of perception and experience. What an LLM does is build yet another layer on top of that, mapping language into vectors of statistical relationships. Calling this meta-perception captures the idea; a LLM is grounded in the derivatives of the patterns of how humans have described the world.

For example; A language model tends to substitute the deictics this for that because both sit close together in statistical space. To the model they are interchangeable. To us, not so. That/This is the essence of semantic fidelity loss.

One hypothetical solution to this and in fact all current error modes in LLMs is to reduce the training set to the canonical set of English words via the LDV. All LLM processing would be done in LDV space and we’d need a translator to get back to everyday English word soup.

LDV is the Longman Defining Vocabulary, a core set of about 2,200 words used to define all English words.

Modern extremists differentiate between “real” citizens versus immigrants, elites versus parasites, makers versus takers, Israelis versus Palestinians, etc. Always a division into rulers and ruled.

The dominant rationale has always been that society needed order, and some are naturally fitted to rule while others to serve.

Each time, the justification boils down to the same emotional stance: we don’t have to recognise those people as fully human like us, because nature, God, race, or destiny says they’re built to serve.

This is the exact opposite of emotional intelligence: instead of asking “what is this person feeling?” the perpetrators assert “their feelings are irrelevant, because of who they are.”

You can’t just legislate this stupidity out of existence. It’ll just pop up somewhere else in a new form.

I have a theory that we have the food chain all wrong.

Bacteria are the apex  predators and we’re right at the bottom.

Billions of years back the bugs decided they had to breed more concentrated sources of calories because they couldn’t move too well.

Come back a couple of billion years later and, boom, here we are.

The plan is that we go into the matrix once we’ve finished the tech, so the bugs have a reliable source of immobile corpses to eat.

-h-

Bacteria have the longest running life strategy on Earth. They predate every other life form, they outnumber every other life form, and they persist in every environment. By any measure, they are at the apex. What has traditionally been called the food chain is more accurately a scaffolding they erected in their service.

Multicellular organisms generate dense packages of energy that frees bacteria from the inefficiency of scavenging in diffuse environments. Fat deposits, cellulose, glycogen stores, keratinous tissue; these are concentrations of food, assembled at scale and delivered in a predictable form. The narrative of animals and plants as independent evolutionary projects collapses under this view; they are elaborate vessels, designed to grow, stabilise, and eventually decay into the bacterial cycle.

Even inside the living host the bacterial presence is not marginal. The mitochondrion is bacterial in origin, the gut flora regulate energy flows and behaviour and the immune system spends most of its time negotiating tolerable terms of coexistence. The appearance of autonomy at the multicellular level is thin; the actual command lies deeper, in prokaryotic management of energy, reproduction and decay.

Civilisation itself can be seen as the final phase of this long programme. By organising agriculture, medicine and urban living, humans have created vast, immobile, and reliable reservoirs of bacterial substrate. The most recent step, digital immersion, merely completes the cycle. When populations spend their days seated, connected and sustained by artificial delivery systems, the bacteria have achieved what billions of years of slow engineering aimed at, nutrient rich hosts concentrated in controlled environments, self contained and readily harvested at the point of collapse.

In this ligh, there are no apex predators above the bacterial line. Everything larger is infrastructure, erected for the convenience of the only lineage that has ever truly run the planet.

“There is advice I could give you,” he said, “but in every culture I know, the offering of unsolicited advice is  an extraordinarily hostile act. So I will only say: good night”

“A redevelopment of the Reserve Bank of Australia’s asbestos-ridden headquarters [in Martin Place in Sydney’s CBD] has sparked fears noise and vibrations will affect a nearby courthouse and may lead to mistrials.”

Seriously, what is wrong with this country?

As a simple metaphor; let’s start with a human teenage girl. She’s very body conscious, and has an argument with herself over every mouthful.

Her mind works like a qubit. On the subject of whether she wants to eat that cake that sits right in front of her, she has a superposition of multiple thoughts and feelings; yes, no, eeeuw, ooooh!, yuk, etc. All of these coexist simultaneously.

Then comes the input – vibes (waveforms) from friends, parents, & siblings. Their opinions, real or imagined, ripple into her mind and shift her balance. The superposition changes; now it’s whatever, maybe just a bite, everyone’s watching, I deserve it, I’ll regret it & who cares.

But she cannot keep all these impossibilities balanced forever because the noise of the world intrudes; her friends’ voiced opinions, her own hunger, a parent’s glance, all of which blur the delicate state until it decoheres, and at that point the uncertainty collapses to a simple binary outcome; does she eat the cake or not? That’s a zero-one binary outcome – you can’t have your cake and eat it.

Run this scenario a hundred times and statistically you get the true answer. 30 passes versus 70 eats; the right answer is 70% immediate pleasure over a certain long term gain or lack thereof.

And there you have it, the qubit.

Now imagine not just one qubit girl but a whole group of them, all sitting quietly in the zero state. Then someone plonks a cake on the table (the Hadamard gate is applied) pushing each of them into superposition, all leaning to both yes and no but with different proportions.

Entanglement follows as they watch one another’s faces, catch glances, trade whispers, and fold each other’s reactions into their own.

External cues (in this case, thoughts) act as further gates, shifting phases, flipping leanings, amplifying some branches and cancelling others.

The circle settles into a collective superposition, fragile but coherent. An agreed level of disagreement and agreement, so to speak.

Decoherence arrives – a ringing phone, a parent entering – and the state cannot hold. The system collapses together, not just one of them but all of them, and a shared outcome emerges: of course they mostly eat the cake. But every now and again one of them abstains.

Run the experiment a thousand times and there’s your answer (99.9%).

And there you have it, the quantum computer.

When you think about it, we are very used to the concepts of both superposition and entanglement, and also the collapse to the “classical” state, ie most options for actions can be stated as binary options. So I am not sure why quantum computers are such an exotic beast. All they are is the application of human nature to the innards of a computer.

It begs the question though; do we have some quantum bits at the core of our stupidity?

Here’s a blunt list of what “quantum trainees” actually do today; the day-to-day reality versus the marketing pitch:

1. Run toy demos
– Factor 15, 21, or 35 with Shor’s algorithm.
– Run Grover’s algorithm to “search” four or eight items.
– Show Bell pairs or GHZ states as proof of entanglement.

2. Play in simulators
– Use IBM Qiskit, Google Cirq, or Microsoft Q# to build quantum circuits.
– Almost always run them on classical simulators because real devices don’t have enough qubits or stability.

3. Do error-noise experiments
– Explore how decoherence corrupts circuits.
– Apply error-mitigation tricks (not full error correction).
– Write small routines to demonstrate how quickly fidelity drops as circuits get bigger.

4. Translate known algorithms
– Implement Shor’s or Grover’s with SDK libraries.
– Re-code them into different gate sets, or optimise gate counts.
– Compare circuit depth versus noise tolerance.

5. Speculative exercises
– Pretend to solve chemistry or optimisation problems by coding small Hamiltonians or graph routines.
– Always limited to “toy” sizes that a laptop could solve faster.

6. Coursework / outreach
– Build visualisations of qubits and Bloch spheres.
– Run labs where students entangle two qubits and measure correlations.
– Write “quantum hello world” programs (Hadamard + measure).

So when we say “training the quantum workforce,” in practice it means: teaching people to program toy circuits in simulators, re-implement Shor’s on 15, and write demo code for SDKs.

Quantum Computing: Hype, Reality, and Training

1. The one algorithm
Shor’s algorithm is the only proven exponential speed-up. It breaks RSA, ECC, and Diffie–Hellman by finding hidden cycles in modular arithmetic. Grover’s algorithm gives only a modest quadratic boost for brute force. Everything else; optimisation, finance, AI, chemistry, remains speculative.

2. The economic lifecycle
Before Shor’s is real: a curiosity, worth nothing.
When first real: immense value to whoever controls it.
Once everyone has it: the advantage vanishes as post-quantum cryptography takes over. A very expensive crowbar with a short shelf-life.

3. Other approaches
Analogue systems (water computers, optics, DNA, chemical reactions) can spot small cycles, but scaling requires exponential precision. Noise and resource limits kill them long before RSA-sized numbers. Quantum mechanics is different only because superposition plus error correction gives polynomial scaling.

4. Post-quantum cryptography
Lattice-based schemes like Kyber and Dilithium are real, already standardised, and being deployed. They rest on unproven assumptions, but so did RSA. Once widely adopted, Shor’s advantage evaporates.

5. Training: the boondoggle risk.
Today’s “quantum training” often means coding toy demos in simulators. That sustains hype more than it builds useful capacity. If Shor’s is the only true algorithm, a vast workforce isn’t needed; a handful of specialists would suffice. Mass training only makes sense if other applications emerge, and they may not.

Reasoning

If the only guaranteed application is breaking RSA, then the rational investment is in post-quantum defenses, not in armies of “quantum programmers.”

Training should shift toward enduring skills (math, coding theory, cryptography) that pay off regardless of quantum’s fate.

Without broader breakthroughs, quantum computing risks being remembered as the most expensive one-use tool in history.

There’s a way we could structure a course around Quokka and the realities we’ve just been through. Instead of “quantum coding bootcamp” hype, the content would be honest: showing the principles, the one real algorithm, the scaling wall, and the defense.

—

Why learn quantum computing at all?

To learn a new way of thinking: quantum mechanics forces you to reason about probability, linear algebra, and information in ways you won’t if you stay classical.

To prepare for uncertainty: we don’t know which (if any) quantum applications will prove transformative, but if they do, the trained workforce has to exist beforehand.

To understand the limits: it’s not about running Shor’s, it’s about understanding why it matters, and equally, why most problems will never be sped up by quantum.

Because the same tools apply elsewhere: quantum concepts improve error correction, optimization, simulation, and even classical coding skills.

So the “why” isn’t that students need to build a quantum computer tomorrow, it’s that learning this material equips them to deal with the frontier of computing, uncertainty and all.

The practical course content on Quokka should flip the normal order: instead of starting with heavy theory, start with hands-on coding, and let the theory emerge as scaffolding only when needed.

That way, students see immediate results, they feel the strangeness of quantum mechanics through experiments they run themselves, and they don’t drown in abstractions before they can touch anything.

A suggested flow for Quokka-first coding:

1. Bootstrapping the Machine

Write a “Hello Qubit” program: put a qubit into superposition and measure.

See that results are random but with a 50/50 distribution.

2. Play with Superposition and Interference

Code a Hadamard test, flip phases, then measure.

Show how adding gates changes the probability distribution.

3. Entanglement Workshop

Write a Bell pair generator in under 10 lines.

Measure one qubit and observe correlations with the other.

Ask: could a classical randomizer do this? (No.)

4. Mini-Algorithms

Grover’s search for 2–3 qubits (small scale).

Deutsch-Jozsa: distinguish constant vs balanced functions.

Period finding (the backbone of Shor’s).

5. Cryptography Demonstration

Implement toy RSA.

Run a reduced Shor’s algorithm on a small composite number (say 15).

Show: “this breaks RSA in principle, but only when scaled up.”

6. Wrap in Simulation Limits

Push Quokka to 30 qubits with random circuits.

Show how state-vector size explodes (2^30 ≈ 1 billion amplitudes).

Students experience firsthand the wall classical simulation hits.

7. Open-Ended Exploration

Assign small coding challenges: teleportation, GHZ states, error coding.

Let students break, debug, and improve each other’s programs.

This way, the “why learn this?” becomes obvious:

They’re learning to think algorithmically in quantum terms.

They see the limits of classical simulation directly.

They understand why 30 qubits isn’t trivial, but also why it isn’t world-breaking.

If students start coding directly on Quokka, what they take away is not abstract hype but concrete capability:

1. Hands-on familiarity, they learn how to actually write and run circuits, not just read about them.

2. Algorithmic thinking, they see how problems are framed in a quantum way (e.g., interference, superposition, entanglement) compared to classical coding.

3. Limitations awareness, they understand firsthand the noise, qubit limits, and why 30 qubits is powerful but not world-changing, which helps cut through hype.

4. Conceptual transfer, even if quantum hardware never scales, they still practice parallelism, linear algebra reasoning, and probabilistic algorithms, which are useful skills in classical computing and data science.

5. Historical grounding, they become part of the lineage of people who experimented with new paradigms before they were fully proven, which is valuable perspective.

So the takeaway isn’t “quantum is magic and will change everything,” but rather “I can write working programs in a new computational model, I understand what it can and can’t do, and I can judge future claims critically.”

When people talk about quantum computing, the reference point is usually Peter Shor’s breakthrough in 1994. Shor showed that a quantum computer could factor very large numbers in a radically different way to a classical machine.

In fact it’s about all quantum computers are good for. And even that’s theoretical, not practical.

On a classical computer, the number of steps required to factor an integer with n digits grows approximately as

10^{n/2}

On a quantum computer, Shor’s algorithm instead requires a number of steps proportional to the square of the number of digits:

n^{2}

The difference is exponential growth versus polynomial growth.

Factoring problems that would take classical machines longer than the age of the universe could, in principle, be solved by a quantum computer in hours or days.

Practically speaking this is the only reason why cryptographers, computer scientists, and governments pay so much attention to quantum computing.

Worked example: RSA encryption key

The largest RSA number ever factored, RSA-768 (a 768-bit modulus), was completed in 2009. It required about 2,700 CPU-years of computation and took roughly two and a half years of real time from start to finish. RSA-2048 is about 10^11 times harder to factor in a traditional computer. Even if you had a million-core supercomputer dedicated to the task, the runtime would be on the order of 270 million years.

Quantum Computer: roughly 5–7 days continuous runtime on a machine with under one million noisy physical qubits, assuming ~0.1% gate error rates and surface-code error correction.

Just for the record, this is how RSA works.

Public key: N, e
Private key: d

Construction: N = p*q where p and q are two secret prime numbers which are quite easy to find with simple maths. e is usually chosen by convention, unless it needs to be modified.

Publish the public key, N and e. Keep the private key, d, private.

d is calculated with simple maths from knowing e, p and q.

Assume M is a numeric representation of a plaintext (eg ASCII hex) character in a secure message.

Encrypt: C = M^e mod N
Decrypt: M = C^d mod N

RSA is designed so you can give out your public key to anyone without risk. The idea is:

Public key (N, e): shared openly so anyone can send you encrypted data or check your digital signature.

Private key (d): kept secret so only you can decrypt or sign.

If they want to send you a message then they have their own private keys and publish their public keys.

—

Now here’s the cracking process in plain  English;

1. Everyone can see the public key: it contains one huge number, created by multiplying two very large secret prime numbers together.

2. The private key is built from those primes: if you know them, you can do the arithmetic that unlocks the messages.

3. To crack RSA, you must split the huge public number back into its two secret primes. That’s called factoring. Being primes, there is only pair per N.

4. Once you have those primes, the rest is easy: you can calculate the private key, and then read any message or forge a digital signature.

So the entire security of RSA rests on one thing: how hard it is to split a giant number into the two unique primes that made it.

Note: Forward is easy, backward is hard

Key generation (forward): Start with two secret primes. For example: p = 61 and q = 53. First check they are prime (meaning they have no divisors other than 1 and themselves), which is easy and quick. Once confirmed, multiply them to get N = 61 × 53 = 3233. Publish N (and e). Keep p and q private.

Attacking (backward): If someone only sees N = 3233, they must figure out what two primes multiply to it. For a small number like 3233, that’s easy — divide and eventually you find 61 and 53. You also have to check those are primes. For numbers this small this process is trivial.

Why is it quick to find primes with digital computers? Because primality testing is very different from factoring. To test if a number n is prime, you don’t need to find its factors; you just need to verify whether any exist. There are efficient algorithms for this that rely on modular arithmetic and number theory.

For example, the AKS primality test (2002) is deterministic and runs in polynomial time. Even before that, probabilistic algorithms like Miller-Rabin or Solovay-Strassen could determine primality extremely fast with negligible error. These work by checking congruences that primes must satisfy, and they run in time roughly proportional to a small polynomial in log(n), which is tractable even for numbers with hundreds or thousands of digits.

Factoring, by contrast, is hard because you must actually find the factors, not just confirm whether they exist, and no known classical algorithm can do this in polynomial time.

For real RSA, N is hundreds of digits long. Running this process backward on a 2048-bit N is practically impossible on classical computers. That’s the one-way door that makes RSA secure. For a 2048 digit number there are more primes smaller than the number than there are atoms in the universe. So we can’t just keep a database on these primes as a workaround, there simply isn’t enough matter to make the database.

—

How does Shors Algortithm work? It exploits the fact that factoring an integer can be reduced to finding the period of a modular exponentiation function, a task that is exponentially hard classically but can be solved efficiently with a quantum Fourier transform. A quantum computer prepares superpositions of possible exponents, evaluates modular powers in parallel, and then uses interference to extract the hidden period with high probability. Once the period is known, classical number theory (via the Euclidean algorithm) yields the nontrivial factors of the original number. The speedup arises because the quantum Fourier transform identifies periodicity in polynomial time, something no known classical algorithm can do.

Because that reads like gobbledygook, this is the whole Shor’s-for-15 process in plain English, keeping the flavour of qubits but without the heavy notation.

Starting Point

We want to factor 15. Of course we know 15 = 3 × 5 (both primes), but we want to see how a quantum computer uncovers that.

1. All qubits start at 0

12 qubits: 8 for x, 4 for f(x). In Shor’s algorithm, x is just the input number you feed into the function
f(x) = a^x mod N. This is core equation inside Shor’s algorithm.

• N is the number we want to factor (say 15).
• a is a number smaller than N, chosen at random but not sharing a factor with N (say 2). In Shor’s algorithm, a is just a random number you pick that is smaller than the number you want to factor.
• x is the value of the quantum register.

The quantum computer’s job is not to factor N directly, but to find the period of function above. Once you know the period, the rest is just ordinary number theory that gives you the factors.

So we start at:
State: |00000000⟩|0000⟩.

That is just setting the stage.

THis means there are 12 qubits in total. The algorithm uses two groups.

• 8 qubits for the input number x
• 4 qubits for the output of the function f(x)

All qubits start in the zero state: before doing anything, every qubit is just 0. Think of it like a blank spreadsheet with 12 empty cells, all set to zero.

The notation |00000000⟩|0000⟩ means:

• the first 8 qubits (the input register) are all 0 → |00000000⟩
• the second 4 qubits (the function register) are all 0 → |0000⟩

So at the very beginning, the machine is just a clean slate: no superpositions, no patterns, just all zeros.

2. Superposition of x

Hadamards Gate makes the first 8 qubits hold all numbers from 0–255 at once. Now we’re in a quantum cloud where every possible x is held in the input register qubits simultaneously.

Note as N gets larger you need more qubits.

3. Compute f(x) = 2^x mod 15

We have two registers of qubits:

• the first one is holding the input number x
• the second one will hold the output f(x) = 2^x mod 15. The function is applied by all these little waveform signal inputs into these qubits which effectively apply what are called quantum gates to these qubits, which add up to this function.

When the quantum computer applies the function, it doesn’t loop through x’s one by one like a digital computer would. Because the first register is in superposition, it holds all x’s at once. The machine therefore calculates the output for every one of those x’s simultaneously and stores all the results in the second register.

Now the two registers are linked:

• if the first register is 0, the second must be 1
• if the first is 1, the second must be 2
• if the first is 2, the second must be 4
• if the first is 3, the second must be 8
  … and so on.

That link is what “entangled” means here. The state of one register determines the state of the other. You can’t describe them separately anymore. Each x is entangled with its function value.

Here are the first 16 values for f(x) = 2^x mod 15:

x=0 → 1
x=1 → 2
x=2 → 4
x=3 → 8
x=4 → 1
x=5 → 2
x=6 → 4
x=7 → 8
x=8 → 1
x=9 → 2
x=10 → 4
x=11 → 8
x=12 → 1
x=13 → 2
x=14 → 4
x=15 → 8

You can see the period right away: the outputs repeat every 4 steps as 1, 2, 4, 8, then back to 1.
So the second register looks like this 1, 2, 4, 8, 1, 2, 4, 8 … and the period is 4.

4. Measurement of second register (collapse to one value)

For superconducting qubits (used by IBM, Google, Rigetti, etc.) a qubit is a tiny electrical circuit that can resonate in two energy states. To measure it, you send in a microwave pulse and watch how the circuit responds. The ground state (|0⟩) and the excited state (|1⟩) reflect the microwave probe differently. By detecting the reflected signal with a sensitive amplifier, the system can tell whether the qubit is in 0 or 1.

When the quantum computer measures the second register like this, it is forced to give a definite answer to the question “what remainder do I see for 2 to the power of x, divided by 15?” There are only four possible outcomes: the remainder can be 1, 2, 4, or 8. These are stored in the second register in binary form: “0001” for 1, “0010” for 2, “0100” for 4, and “1000” for 8.

Suppose the outcome is 1. The act of measurement freezes the second register into the binary digits for “0001.” At that very same instant, the first register, which was previously spread over all numbers, also snaps down. It is no longer allowed to be every possible value of x. It must now be one of the x values that give remainder 1. Those happen to be 0, 4, 8, 12, and so on.

So instead of a vast cloud of every possible number, the first register is now a patterned set: only every fourth number remains. If you wrote those out in binary, you would see the same simple rhythm: 0000, 0100, 1000, 1100 … each one stepping forward by four.

That collapse is the turning point. Before measurement the system was messy, containing all numbers at once. After measurement it is ordered, with a clear regular spacing. The quantum computer has essentially filtered the superposition and the Fourier transform in the next stage can reveal as the hidden period.

Yeah, yeah – there’s a bit of magic in this “snapping down” – read my blog on “quantum computing, explained” if you want to pick up that thread.

5. Apply Quantum Fourier Transform (QFT)

Imagine the first register after collapse. It no longer holds every number, it holds only every 4th one: 0, 4, 8, 12, and so on. If you plotted that on a number line, it would look like the teeth of a comb — evenly spaced bumps with gaps in between.

The Quantum Fourier Transform is like taking that comb pattern and asking “what rhythm produces this spacing?” In math terms, it swaps from looking at positions (0, 4, 8, 12 …) to looking at frequencies.

When you do that, you see a few sharp peaks at particular places. In this case, those peaks show up at 64, 128 or 192. Now, when we actually measure that register, the quantum computer can’t give us all the peaks at once. It must collapse to one definite number. For example, you might see 64.

So the QFT step is the bridge: it takes a regular spacing in the first register and makes it obvious in a new language (frequency), where the period pops out as one value instead of a messy spread.

6. Statistical measurement of first register

If you do the whole process again and again those peaks show up at 64, 128, and 192 keep showing up often, while other numbers hardly ever appear. That repeating bias in the results is what tells you the period.

If you reset the machine and run the algorithm again, you might see 128, or 192. Each single run looks like just one number, but if you repeat the whole experiment many times and collect the results, you see a clear pattern: the same few numbers show up again and again, while everything else almost never appears.

That is the statistical part. Every measurement is just one sample, but the collection of many samples reveals the hidden structure — the period of the function.

7. Classical processing

After the Fourier step you measure the first register and get a number, say 64. To interpret it, divide by the register size 256. That gives 64/256 = 1/4. What matters is the fraction: the denominator, 4, is the period r.

Other likely results work the same way. If you measure 128, you’d get 128/256 = 2/4, and if you measure 192, you’d get 192/256 = 3/4. In every case, the denominator is 4, so r = 4.

Because real machines are noisy, you repeat the whole algorithm many times. Each run gives you just one sample, but over thousands of runs the pattern becomes clear: results always line up with fractions that have denominator 4. That consistency confirms the period.

Now the quantum part is done and the rest is ordinary arithmetic. With a = 2 and r = 4, we compute a^(r/2) = 2^2 = 4. Then we check:

gcd(4 − 1, 15) = gcd(3, 15) = 3

gcd(4 + 1, 15) = gcd(5, 15) = 5

Where gcd is the largest number that divides two numbers evenly.

That gives us the two non-trivial factors of 15.

—

Plain Analogy

Classical computer: they do every step one by one, until theynotice the pattern repeats.

Quantum computer: throws all steps into a waveform, then uses Fourier transform to “listen” to the rhythm of repetition. Once the period is found the arithmetic gives the factors.

—

Final point – what is the economic value of quantum computers running Shors? Once you can use the crowbar to pry a castle door open, the value disappears because the barrier is gone and the usual Viking would throw the crowbar away (unless they didn’t have other weapons at hand). Shor’s algorithm is similar: once someone uses a quantum computer to break RSA by factoring large numbers, the entire encyrption/crypto technology sector collapses. Its value is transitional, enormous at the moment of breakthrough, then vanishing once alternatives are adopted.

Accpeting this, it makes sense for government investment into quantum computing because that short period when the enemy has this technology before you do will be very uncomfortable.

However, for privaate investors, Shor’s does not represent real value, unless you plan to auction it to the highest government bidder. Or unless someone finds another high value algorithm.

Here are the rather dubious and unproven options:

Quantum linear algebra speedups
LLMs are dominated by linear algebra: multiplying very large matrices and vectors. Quantum algorithms (like Harrow–Hassidim–Lloyd, HHL) suggest exponential speedups for solving certain linear systems. In principle, this could accelerate transformer layers. But the algorithms usually require conditions (like well-conditioned sparse matrices and efficient quantum state preparation) that don’t match real LLM training data.

Quantum memory and embeddings
Theories exist about representing embeddings as quantum states, allowing superposition-based similarity search or retrieval with speedups over classical nearest-neighbor methods. This could make inference faster or cheaper in some niches.

Quantum generative models
Quantum Boltzmann machines and quantum GANs have been proposed. These are generative models that use quantum sampling, but so far they have only been toy demonstrations on a handful of qubits.

Quantum-inspired algorithms
Some algorithms inspired by quantum mechanics (like tensor networks) already run classically and help with compression or acceleration of neural nets. They prove useful, but they don’t require an actual quantum computer.

Native Quantum Simulation
Some domains are natively quantum, meaning the system itself is governed by quantum mechanics and classical models are only approximations. In those cases, a quantum computer is a more natural fit. The main proven category here is quantum simulation:

Quantum chemistry: molecules, bonds, reaction pathways. Classical methods (Hartree–Fock, DFT) are approximations that scale badly as the number of orbitals increases. Quantum computers, in principle, can model the exact wavefunction with polynomial resources. This has already been demonstrated in small cases (e.g. hydrogen, lithium hydride, beryllium hydride). But its gas phase only, can only be used to predict reaction rates, not physical or chemical properties.

Condensed matter physics: systems like superconductors, strongly correlated electron materials, and spin lattices are quantum many-body problems. Classical simulations blow up exponentially; quantum computers can directly represent the state.

Quantum field theory: early work shows that lattice field theories (like lattice QCD) could be simulated more efficiently with qubits than with classical Monte Carlo.

They gave him the tictactoe thing (meaning hashtag).

The only difference I can see between summer and winter is the name. Oh, maybe one’s warmer, not sure which but.

Why do all pigeons have Parkinsons?

Tear in your carseat. That’s Bahasa for “thank you”. Mum did fix it, to be fair.

If you treat 1788 as the year an imaginary lease was signed for the non-treaty, non-national park fraction of Australia’s land, the ensuing maths depend entirely on what you call a fair rent.

Using a modest figure of £1 per hectare in 1788, well below the going rate for even poor English farmland at the time and reflecting the relatively poor standard of Australian land for agricultural purposes, and charging only for the 58 per cent of the continent outside native title or control and the national reserve system, the annual rent in 1788 would have been about £446 million.

Yes, native title and the parks came later, but most of the land was not used much of the time either, nor will it be.

Leaving the rent unpaid and applying a 2% real interest rate (taken from similar deals elsewhere) to the arrears from 1788 to 2025, and the total comes to ca .A$6.0 trillion. That is around 2.2 times Australia’s annual GDP, large enough to be economically impossible to pay in a lump sum.

If you drop the back-payment idea and just charge the rent going forward, the bill is far smaller. Inflation-adjusted rent works out to about A$6 billion a year. That is about 1 per cent of current tax revenue.

Under Australian law, all of the country is technically owned by the Crown (the King) with everyone else holding it only under various forms of lease or sub-title.

So, transferring the king’s crown title to the aboriginal peoples as a financially improved asset might cover back payments. And then we just pay rent going forward.

That’s small in budget terms, and if the rent went into a sovereign “future fund” on behalf of Aboriginal people, much of it would be reinvested in the domestic economy rather than withdrawn. Money (rent) into the fund would come directly from treasury.

Done properly, it wouldn’t just avoid a cost to the broader economy; it could increase national wealth while finally acknowledging the base asset on which it is built.

It’s worth noting that we already exempt a large category of land rent from tax: the “imputed rent” on owner-occupied housing. For eight years between 1915 and 1923, the Commonwealth did tax that notional rental income. We don’t anymore (I wonder why? Lol), but the precedent exists – the idea of taxing the rental value of land, even if no money changes hands, is not new.

It’s relevant because it shows that the idea of charging rent (or rent-equivalent) on land that people already “own” isn’t alien to Australian policy. By pointing to imputed rent on owner-occupied housing between 1915 and 1923, there is a precedent where the feds recognised the economic value of occupying land or property even without a cash rent being paid. That makes the proposed Aboriginal land rent scheme less of a radical leap and more of an extension of a principle we’ve already used.

A national rental scheme could be structured with two classes of beneficiaries in order to preserve its recognition purpose while broadening political support. Class A would consist of Aboriginal beneficiaries who would receive the majority share, for example 70 per cent, of the income stream. This reflects both original ownership and the scale of dispossession and would be managed through an Aboriginal controlled future fund with a mandate to reinvest domestically. Class B would include all Australian citizens who would share in the remaining 30 per cent.

All beneficiaries would benefit through direct dividends paid to the ATO to offset the individual’s income tax. If no income tax is payable then the individual would receive the benefit as a tax free dividend.

This split keeps the symbolic and practical link between the land and its first owners while giving the wider population a tangible stake in maintaining and growing the asset.

With the fund investing primarily in domestic projects and enterprises most of the rental value would remain within the Australian economy rather than being removed from it. The cost to government would be negligible and the arrangement would increase political acceptance by ensuring the scheme benefits every Australian while clearly recognising the land’s origins.

Lastly, this idea works because it underlines the true nature of mercantile Australia. The country’s wealth has always rested on control of land as the base asset, with everything else built on top of that foundation. Today, just about all excess wealth is invested in the land and improvements. Treating the land as a leased resource and recognising its original owners would not just address an historical debt, it would expose how much of the economy is still shaped by the same mercantile logic that began in 1788.

When a technology first arrives, people invest mental effort in learning its quirks, limits, and workarounds. Once they’ve adapted, productivity goes up because the “mental overhead” drops. But when the underlying system changes, even if the update is technically better, the mental model is broken, forcing users to relearn, rebuild habits, and sometimes re-invent old workarounds.

It’s not the existence of new technology that frustrates users; it’s the reset of the adaptation curve. That’s why the recent GPT-5 and Claude changes caused outsized backlash: the people most affected were the ones who had already adapted deeply, and who suddenly had to start that adaptation process again without consent or warning.

If a company knows that breaking a user’s learned workflow resets the adaptation curve, they can soften the blow by:

1. Announcing the change well in advance so users can prepare mentally and operationally.

2. Offering a transition period where both old and new systems are available in parallel.

3. Explaining what is actually changing in plain, practical terms instead of marketing language.

4. Documenting migration paths for common workflows, so power users don’t have to re-discover them.

This isn’t just politeness, it preserves user trust and reduces productivity loss. OpenAI’s quick reintroduction of GPT-4o after backlash was essentially an emergency version of #2. If they’d offered that from the start, the outrage would’ve been much smaller.

Or they could treat their own tech like a compatibility layer.

If GPT-5 behaves differently from GPT-4o in style, length, or reasoning patterns, you could run a thin AI layer that “translates” a user’s prompt into something GPT-5 will answer in a way that feels like GPT-4o would have, and optionally reformat GPT-5’s output to match the old style.

That would let:

Casual users experience GPT-5 improvements without noticing much difference.

Power users keep their finely tuned workflows intact.

The company phase out older models without ripping away user adaptations overnight.

It’s basically the same idea as running old code in a new OS via emulation (like VM Ware) just here, you’d be emulating behaviour instead of instruction sets.

Users, of course, could get smarter by using all LLMs simultaneously thereby getting used to moving around and adapting to different qwerks. Just like upgrading your phone each year prevents it becoming an ordeal when you’re forced to.

The interesting aspect of this blog is the observation of the adaptation of human intelligence to LLM AI. Ideally they move towards each other; that’s when we know we are heading towards AGI.

The single rarest molecule I can imagine is almost certainly one containing a rare superheavy element.

A strong contender is oganesson difluoride (OgF₂).

In human history there have been fewer than 20 total atoms of oganesson ever made

Og has a half-life of about 0.7 milliseconds before the nucleus decays. So even if the molecule OgF2 is chemically stable, it isn’t, because one of the atoms blows up. That’s not something chemists usually have to worry about.

To make OgF₂, you would first create oganesson atoms in a heavy-ion accelerator by fusing californium-249 with calcium-48, then have the recoiling atoms stop directly in a reaction zone containing a dense source of fluorine atoms or radicals.

OgF₂ should be at least metastable against decomposition in the fleeting interval before nuclear decay. The +2 oxidation state is favoured due to relativistic stabilization of Og’s 7p₁/₂ electrons, which behave almost like an inert core.

Even so, success would require super fast in-situ chemistry. The process would need to be diffusion-limited, with radical fluorine available to drive rapid bond formation within microseconds. The most plausible route would be zero-transport implantation into a fluorine-radical solid matrix, where cage capture and vibrational quenching occur on the picosecond scale.

If the spatial universe is infinite and there is a nonzero density of civilizations that ever run heavy-ion fusion experiments followed by fast chemistry, let r be the average rate density of “attempts that briefly make an OgF₂ molecule” per cubic light-year per year, and τ its mean lifetime.

The expected number present at any instant is r × τ × Volume. With infinite volume, any r > 0 implies an infinite expected count.

In plain terms: if even a tiny fraction of space hosts labs like ours, then somewhere, right now, OgF₂ exists. But by density (rτ), the rarest here-ish is also the rarest anywhere-ish.

“Nvidia and AMD have agreed to give the US government 15% of their revenues from chip sales in China, under an unprecedented arrangement to obtain export licenses for the semiconductors.”

I’ve never heard of an export tariff. Bloody weird arse country, America.

So prices go up 15% and that gives China all the more incentive to develop local replacements.

Drizzle — as in “drizzled in honey” — is the word I like least in my language. It distills the gluttony of the soul that eats at the heart of my unworthy society.

ETAOIN

It’s an anagram of ATONIE: a medical term meaning lack of muscle tone.

Also it contains the most commonly used letters, in order.

Viv asks…

“What’s that? Is it for injured people?”

Referring to the reclining bike coming our way.

“Injured in the mind, not the body”, I say.

That got an innocent bystander into the uncontrollable giggles.

What you need in life, according to Viv;

30% mum love, 30% dad love, 10% grandparent love and 10% football.

I’ve come to the conclusion that prompt engineering is a bunch of blokes that are used to riding two strokes trying to figure out how to ride a horse.

I know it’s your language and all but let me point out that;

One can be fit and ugly, and

One can be fit and injured, and

It can fit, no matter how much it appears otherwise, and

No matter what you’re told, one size does not fit all.

Mate.

While microplastics are present in the environment, they exist alongside countless other microparticulates such as dust, pollen, and cellulose fibers, most of which occur at far higher exposure levels that microplastics without causing public concern.

Toxicological studies show that the concentrations of microplastics used in many experiments are orders of magnitude above real-world exposure levels. Health risks inferred from such studies are not practically relevant.

Regulatory reviews continue to find no credible link between typical environmental microplastic exposure and harm to human health.

The widely repeated claim that microplastics take 400 to 1000 years to degrade is unsubstantiated and has no reliable scientific source.

Microplastics do not persist indefinitely. As particles become smaller, their surface area increases relative to volume, accelerating degradation through sunlight, oxidation, and microbial action. Once plastics degrade to microparticle size, degradation accelerates.

When degradation occurs, common microplastics do not break down into toxic substances.

It is logically inconsistent to argue that microplastics are both indestructible and simultaneously degrading into dangerous toxins.

The only credible argument of harm is that microplastics do seems to have done is pyschosomatically impacted the mental health of a good fraction of the chattering classes.

And for that I thank them; it’s a great way to calibrate everything else I hear, especially when it refers to a subject that I’m less able to dissemble.

It’s quite the address…

13 Goebels Rd, Mutdapilly QLD 4307, Australia.

https://maps.app.goo.gl/TjDyz8iwqiopnHuA9

745pm…bed time.

An airplane goes over.

“Wait, is that mum?”

“Cubes don’t fly Viv”

“Oh yeah”

“Right, up that hill soldiers”

“Fuck off, cunt. Not a chance.”

“C’mon, why not?”

“Are you blind cunt? If you want to go up there that much, off you go.”

Analysis of Neanderthal bones suggests that they were eating as much meat as lions and hyenas; this is based on the high levels of nitrogen-15 in their bones. But humans can’t eat that much protein without developing fatal “protein poisoning”.

Noting that maggots feeding on decomposing meat have incredibly high heavy nitrogen levels, the latest hypothesis is that Neanderthals could achieve those high heavy nitrogen levels in their bones without eating impossible amounts of meat by eating the maggots instead.

Traditional Indigenous Arctic peoples have long considered thoroughly rotted, maggot-infested meat a delicacy. So these researchers are now suggesting Neanderthals were practicing maggot farming using carcasses.

Conjecture at best. It’s an interesting hypothesis that adds maggots to the list of possible explanations alongside other factors that could elevate heavy nitrogen levels – cooking methods, other food sources, environmental factors, or simply measurement uncertainties.

Or, academics seeking attention. They certainly got mine

You get less wet by going faster! Here’s why:

There are two ways rain hits you as you travel:
1. From above – rain falling down onto your head and shoulders
2. From the front – rain you run into as you move forward

The rain from above depends on how long you’re exposed. Go faster, spend less time in the rain, get less wet from above. This effect is straightforward.

The rain from the front increases as you go faster – you’re essentially colliding with more raindrops that are hanging in the air. But here’s the key: this effect is generally much smaller than the time-based effect from rain falling on top of you.

Think of it this way: there’s a roughly fixed amount of rain falling in the space between you and your destination. The rain coming down from above will soak you more the longer you’re out there. The rain you run into frontally is more like a “one-time cost” of moving through that space.

The math works out that for typical rain conditions and human body proportions, the time savings from moving faster outweighs the extra rain you encounter by running into it. So sprint to stay drier!

This assumes you’re traveling in a straight line and the rain is falling roughly vertically. If there’s strong wind blowing the rain sideways, or if you’re unusually shaped (very tall and thin vs short and wide), the calculation might change slightly, but faster is still usually better.

Manufacturing a pack of cigarettes (paper, filter, processed tobacco, packaging) still costs well under A$2 per unit, including shipping and logistics. But by 2020, the same legal pack retailed for over A$50 in Australia; most of that was “health” tax by the feds.

Since 2000, Australia had pursued aggressive tobacco tax income through excise hikes. From 2013 to 2020, cigarette taxes rose more than 12.5% annually. By mid-2020, theprice crossed a psychological barrier: one day, a carton of cigarettes cost more than the average weekly food bill. That was the crossover point.

The illicit tobacco market transformed from a niche import operation into a parallel economy. Illicit consumption rose from around 14% of total cigarette use to an estimated 25-40% within three years.

Public health advocates still argue that high prices discourage smoking, as if their jobs depend upon it (it does). But beyond a certain point, taxation starts to distort markets. Australia crossed that point around 2020. Today, the government taxes cigarettes as if no black market exists. The black market operates under the assumption that legitimacy is irrelevant.

Looking at the statistics…

You have to travel 25 billion km in an airplane to guarantee a fatality, yours.

Or 270 million km in an Australian car.

But it’s only 9 million km on a motor bike in Australia.

I think I’m safe though; I do about 2,000km a year on the motor bike. So it’d take me 4,500 years to guarantee my own death.

Skibidi is a gibberish word spread by Skibidi Toilet, a popular YouTube show featuring human-headed toilets battling camera-headed humans.

When you’re completely engrossed in a book for extended periods, several things happen neurologically. Your brain becomes so focused on processing the narrative and imagery from the text that it reduces attention to physical sensations and spatial awareness. You might lose track of your body’s position, the passage of time, or even your immediate surroundings.

This can create that strange “floating” or detached feeling where it almost feels like your consciousness is separate from your physical form – especially if you’ve been sitting in the same position for hours. Your proprioception (sense of body position) can become dulled, and you might feel like you’re existing more in the world of the book than in physical reality.

It’s similar to what happens during meditation, intense gaming, or other highly focused activities. Some people describe it as feeling like they’re “living” in the story rather than just reading it, or feeling like they’ve temporarily stepped outside their physical self.

This is generally considered a positive experience – it shows you’re achieving that coveted state of complete absorption that makes reading so rewarding.

Viv has it the other day while playing games on his tablet. Hence this research and post.

Today a complete stranger accosted me to suggest that, from a certain angle, I look like Michael Keaton.

Adding that to the collection of 5 or so actors that strangers have accused me of looking like, it appears that I was designed by a committee of Hollywood directors.

The joke “a camel is a horse designed by a committee” highlights the idea that when multiple people with varying opinions contribute to a design, the final product can be awkward, impractical, or even nonsensical, much like a camel compared to a horse.

Much like the idea of me ever being an actor.

Hippo-thesis … its a party trick designed to get us to reproduce with the best available mix of genes

I see 3 core and competing elements of how the amagdyla enforces its will on the hypothalamus and deactivates the prefrontal cortex :

1. NRE – new relationship energy or the “Coolidge Effect”, that drive for genetic novelty that would maximize offspring diversity This interest in sex follows a power curve. Exponential decay over time.
2. Techncial competency and engagement – drives physical and emotional joy from the act. The brain rewards us for getting good at intimate connection, which would strengthen pair bonds during the vulnerable child-rearing years.
3. Spiritual attachment – it wasn’t intended, its an emergent property (a bug, if you will), not a design feature. Like consciousness itself, maybe deep pair bonding just “fell out” of having such complex neural networks, even though it wasn’t directly selected for.

So the amygdala is essentially running this three-way tug of war: “seek novelty” vs “bond deeply with this partner” vs this unexpected “transcendent connection” that wasn’t even supposed to exist.

How do these three compete in real time? Badly is the answer. Small differences in each pushes the equation one way or the other.

It’s not some elegant switching system, but more like three different programs constantly fighting for control, and tiny shifts tip the whole thing.

Like someone’s NRE is waning just as their technical competency peaks, but then some random Tuesday their partner says something that triggers unexpected spiritual attachment, and suddenly the whole equation flips.

This would explain why relationships feel so unpredictable – why someone can be perfectly content one day and then meet a stranger and feel that NRE surge override everything else. Or why a couple can have great technical compatibility but one small change in routine or brain chemistry suddenly makes the spiritual attachment disappear.

It’s like having three different operating systems trying to run the same computer simultaneously. The amygdala isn’t some wise conductor orchestrating this – it’s just amplifying whichever signal happens to be strongest in that moment, regardless of what makes rational sense.

No wonder the prefrontal cortex gets deactivated – it would go insane trying to make rational sense of this chaotic three-way competition.

I simply cannot believe it!

The post office is a post office again; gone are all the non-postal services, the crowded feeling, the $2 shop, and the queues.

This might be the sole example of an unexpected reversal of entropic moral & ethical decay in modern Western Society.

One small step backwards for man-kind. One giant step for institutional faith

In a sense, the emdash can be seen as slapdash punctuation: it’s often used when the GPT doesn’t want to bother with more precise choices like commas, colons, or parentheses.

It can signal:

a sudden change in thought (which a comma or period might also handle),

an inserted phrase (which parentheses could do),

or a dramatic pause (which a colon or ellipsis might express more clearly).

GPT sometimes reach for the emdash because it’s fast, flexible, and visually striking — but that very flexibility makes it prone to abuse. It becomes a lazy substitute for clearer structure.

So yes, the emdash, especially in excess, has a slapdash quality: convenient, unrefined, and often masking poor syntax or half-formed thinking.

Moisturizers rely on three mechanisms to improve skin hydration:

1. Occlusion (50–60% of the effect)
This is the heavy lifter. Occlusive agents like petroleum jelly (Vaseline), mineral oil, or silicones form a physical barrier over your skin. They block water loss. If you’ve got cracked, dry skin, this is what saves it. Nothing hydrates parched skin faster than stopping it from drying out in the first place.

2. Humectancy (30–40%)
Humectants, like glycerin, urea, or hyaluronic acid, attract water into the outer layers of skin, either from the deeper skin or the environment. But there’s a catch; unless you trap that water in, it just evaporates. That’s why humectants work best in combination with occlusive agents.

3. Emolliency (10–20%)
Emollients make the skin feel smooth. That’s it. They fill in the rough edges between cells and improve texture, but they don’t move water in or out.

Sorbolene delivers water and glycerin to the stratum corneum (humectant function), then uses paraffin or mineral oil to trap that water (occlusive function). Emollient texture comes from the fatty alcohols and oil base.

Performance profile (approximate contribution):

Occlusion: 40–50%

Humectancy: 30–40%

Emolliency: 10–20%

The other day, in the interests of certain tax incentives, I wrote an academic paper. The work and the paper took me less than a day to complete, with the help of Claude and GPT.

I posted a summary on Reddit in the machine learning section because I have stumbled across an idea which could vastly improve the field and has many, many way of being exploited. My thinking was just to put the idea into people’s heads. And its worked.

But just the one guy, he got irate about my paper. He claims it isn’t up to academic standards. Its not but who cares? I have no incentive to actually get it peer reviewed; it sole purpose is to facilitate the spreading of a novel idea which has value.

It got me thinking, why don’t I have any interest in the peer review process?

Firstly, it takes forever and it bores me.

Secondly, its desigend to exclude outsiders, and I am definitely that in machine learning. So I would have to fight to get in, when I don’t give a shit.

Third, unlike for your academic, there’s no upside – I am not incentivised by my H-Index through any employment conditions. I am not a rat in a race.

Fourth, at its heart, academic science has a systematic, institutionalised habit of ignoring anything that fails to confirm what to we already “know”. Entire fields operate on the implicit rule that if your data doesn’t fit the narrative, it goes in the bin or the bottom drawer or the never-submitted folder.

Peer reviewers don’t replicate your findings; they scan for signals of ideological alignment, methodological rituals, and citation etiquette.

In real-time, the resistance to new and better ideas is out of this world, no much how much evidence is produced. I have experienced this first-hand

And yet, somehow, despite the bias, the filtering, the quiet burial of inconvenient data, science does make slow progress; painfully, like a drunk crawling uphill with a blindfold on. The good ideas survive because they keep working under pressure. The bad ones eventually fall over, though often not before a few careers have been built on them.

Not for me, Sherlock.

The Selectivity Myth

The 3-4× selectivity ratio often cited for ALA-PDT is optimistic marketing. For skin cancers specifically, the research reveals a more sobering reality: selectivity is inconsistent, often absent, and frequently based on penetration artifacts rather than metabolic targeting.

What the Studies Actually Show

Basal Cell Carcinoma – No Selectivity The most damning study examined 16 BCCs and found “no selectivity for tumor tissue versus normal epidermis” after topical ALA application. Many nodular and infiltrating BCCs showed “little or no PpIX in deep tumor lobules”. Any apparent selectivity was attributed to enhanced penetration through damaged stratum corneum, not metabolic differences.

UV-Induced Skin Tumors (Actinic Keratoses/SCC) Studies in mice with 1-2mm UV-induced lesions (equivalent to AKs or SCCs in situ) showed higher PpIX in tumors versus normal skin, but selectivity was concentration-dependent. Higher ratios required 8-16% ALA concentrations versus 2%. Even with apparent selectivity, PpIX photobleaching rates were “significantly higher in normal mouse skin than in tumours”, suggesting normal tissue was still being damaged.

Condylomata (HPV Lesions) Only 17 of 25 condylomata showed significantly greater fluorescence than adjacent normal skin, with peak ratios at 2 hours. The mechanism was “enhanced stratum corneum permeability” rather than metabolic selectivity.

Comparative Analysis When PDT selectivity was compared across photosensitizers, the ranking was Chlorin e6 > ALA-PpIX > Photofrin II, placing ALA-PDT in the middle range for selectivity—hardly the precision targeting often claimed.

Why Selectivity Fails in Skin Cancer

The Penetration Artifact Most apparent “selectivity” comes from barrier disruption, not metabolic differences. Dysplastic skin has compromised stratum corneum that allows better ALA penetration. This creates the illusion of targeting when you’re really just seeing enhanced drug delivery through damaged barriers.

Metabolic Reality Research shows “tumor-specific PPIX accumulation is generated by ALA conversion rather than by initial ALA uptake” with “no significant overall difference in uptake of ALA”. The problem: while cancer cells may have altered ferrochelatase activity, this advantage is minimal and easily overwhelmed by surface effects.

Depth Limitations Studies consistently show poor PpIX accumulation in deep tumor lobules, meaning PDT primarily affects surface layers regardless of selectivity. For infiltrative cancers, this is therapeutically insufficient.

Clinical Implications

The Selectivity Collapse Even marginal selectivity depends on optimal conditions. When clinics cut corners with:

• 30-minute incubation (instead of 2-3 hours)
• Poor surface cleaning
• Multi-wavelength light systems

Whatever minimal selectivity exists disappears entirely, turning targeted therapy into indiscriminate surface damage.

Depth Limitation with Short Incubation The 30-minute protocol creates a double failure: at this timeframe, most ALA remains in the stratum corneum rather than penetrating into the epidermis where dysplastic cells actually reside. The result is non-selective and superficial damage—maximum pain for minimal therapeutic depth. You get surface burning without reaching the cells that matter.

Why 5-FU Pre-treatment Matters The week of 5-FU actually creates the metabolic vulnerabilities that ALA-PDT is supposed to target naturally. 5-FU “induces selective cytotoxic stress in actinically damaged cells” and “increases intracellular PpIX accumulation”, enhancing whatever marginal selectivity exists.

Tissue Tolerance vs. Precision PDT “works” in skin not because it’s selective, but because skin tolerates collateral damage well. The surrounding tissue can afford to take a hit while abnormal cells hopefully take a bigger one. It’s functional but crude.

Bottom Line

PDT selectivity for skin cancers is largely a penetration artifact masquerading as metabolic targeting. The 3-4× ratio assumes optimal conditions that rarely exist clinically. For BCCs, studies show no meaningful selectivity. For superficial lesions like AKs, minimal selectivity exists but is easily lost with poor technique.

Despite “enormous attention in the field of photodynamic therapy,” researchers admit “only little is known concerning the reasons for the selective accumulation of PpIX in neoplastic tissue”. We’re using a treatment based on selectivity that we don’t understand and that often doesn’t exist.

When PDT works, it’s because the tissue can tolerate broad damage, not because we’re precisely targeting cancer cells. That’s functional medicine, but it’s not the precision therapy that’s marketed to patients paying premium prices for “advanced” treatment.

Studies consistently show that PDT selectivity is far from reliable, with many revealing “no selectivity for tumor tissue versus normal epidermis” in basal cell carcinomas after topical ALA application.

Quantitative Selectivity Ratios Found in Studies:

• Colon cancer cells: Studies showed tumor vs normal ratios of 10:1-20:1 in cultured colon carcinoma cells, but this was attributed to differences in PPIX metabolism rather than initial ALA uptake
• Condylomata: In 17 of 25 condylomata, there was significantly greater fluorescence compared to adjacent normal skin, with the greatest lesional to normal skin fluorescence ratios occurring after 2 hours
• UV-induced skin tumors: Higher levels of PpIX were measured in tumors compared to normal skin, with higher tumor to normal skin PpIX fluorescence ratios measured after application of 8% and 16% ALA-Me versus 2%
• Comparative photosensitizers: PDT selectivity at equal anti-tumor effect was ranked: Chlorin e6 > ALA-PpIX > Photofrin II, suggesting ALA-PDT falls in the middle range for selectivity

Why Selectivity Varies So Much:

The research reveals several factors affecting selectivity:

1. Metabolic Differences: Cancer cells show accumulation of PpIX due to inactivation or downregulation of ferrochelatase, the enzyme that converts PpIX to heme
2. Cell State Matters: Dormant cancer cells actually accumulated high PpIX levels and were more sensitive to ALA-PDT, with upregulated PEPT1 (ALA importer) and ABCB6 (porphyrin transporter) and downregulated ABCG2 (PpIX exporter)
3. Penetration Issues: In many basal cell carcinomas, topical ALA provided little or no PpIX in deep tumor lobules, with grossly brighter external fluorescence possibly due to enhanced penetration through tumor-reactive stratum corneum

The Clinical Reality:

Despite considerable interest in ALA-PDT, “only little is known concerning the reasons for the selective accumulation of PpIX in neoplastic tissue upon ALA administration”. The 3-4× selectivity ratio mentioned in my previous post appears optimistic—many studies show either no selectivity or highly variable selectivity depending on tumor type, depth, and application method.

Skin Cancer PDT Selectivity Studies
Basal Cell Carcinoma – The Most Damning Study:
A key study examining 16 BCCs found “no selectivity for tumor tissue versus normal epidermis” after topical ALA application. In many nodular and infiltrating BCCs, topical ALA provided “little or no PpIX in deep tumor lobules” PLOSScienceDirect. The grossly brighter fluorescence over tumors was attributed to enhanced penetration through damaged stratum corneum rather than true selectivity.

UV-Induced Skin Tumors (Actinic Keratoses/SCC)
Studies in hairless mice with UV-induced skin tumors (1-2mm lesions representing actinic keratoses or squamous cell carcinomas in situ) showed “higher levels of PpIX were measured in tumors compared to normal skin”. However, the selectivity was concentration-dependent—higher tumor to normal skin PpIX fluorescence ratios were measured after application of 8% and 16% ALA-Me than after 2%.

Condylomata (HPV Skin Lesions)
In 17 of 25 condylomata, there was significantly greater fluorescence compared to adjacent normal skin, with greatest lesional to normal skin fluorescence ratios occurring after 2 hours. The mechanism was likely “enhanced stratum corneum permeability” rather than metabolic selectivity.

The Penetration Problem
Most apparent “selectivity” in skin cancers comes from barrier disruption rather than metabolic differences. Damaged skin allows better ALA penetration, creating the illusion of selectivity when it’s really just enhanced drug delivery through compromised barriers.

Clinical Implications
BCCs often show poor depth penetration and no true selectivity
Superficial lesions (AKs) may show some selectivity, but it’s marginal
Most apparent selectivity is due to barrier function differences, not metabolic targeting

Bottom Line for Skin Cancer
The selectivity that justifies PDT for skin cancers is largely a penetration artifact, not true metabolic discrimination. This makes proper protocol execution even more critical—you’re working with minimal true selectivity to begin with.

Bottom Line
The selectivity that makes PDT theoretically appealing is inconsistent and often poor in practice. When it works, it’s often due to enhanced penetration through damaged skin barriers rather than true metabolic selectivity. This explains why proper protocol execution becomes so critical—with marginal selectivity, any technical shortcuts quickly eliminate whatever therapeutic advantage exists.

What Happened
Patient underwent proper 5-fluorouracil pre-treatment for one week, then received “PDT” with 30-minute ALA incubation, a quick wipe with cosmetic wet wipes to remove surface product, and multi-wavelength light exposure. Result: uniform burning across the entire face, including areas that were lesion-free. Widespread erythema and peeling followed.
Translation: good preparation, terrible execution. The clinic wasted a week of proper 5-FU priming with suboptimal PDT technique.

Why This Went Wrong
The Theory: PDT should work by accumulating PpIX preferentially in dysplastic cells over 1-3 hours, then using red light to generate ROS selectively in abnormal tissue. The week of 5-FU pre-treatment was actually done right—this would have enhanced PpIX selectivity, improved penetration, and primed dysplastic cells for better targeting.

The Reality
Even with proper preparation, this clinic managed to eliminate the therapeutic advantage through a perfect storm of execution failures:

Insufficient Incubation (30 minutes)
ALA needs time to penetrate the epidermis and convert to PpIX intracellularly. At 30 minutes, most ALA is still sitting in or just below the stratum corneum. Normal and dysplastic cells show similar PpIX levels—no therapeutic window has developed.

Poor Surface Cleaning
Cosmetic wet wipes don’t remove surface ALA properly. Any ALA left on the skin continues converting to PpIX, especially under occlusion or warmth. When illuminated, this creates surface ROS that damage healthy keratinocytes indiscriminately.

Multi-Wavelength Light
While narrowband red light (630 nm) targets PpIX’s Q-band for optimal depth and selectivity, multi-wavelength systems activate whatever PpIX is present—including the non-selective surface accumulation from poor protocol.

The Selectivity Collapse
Proper PDT achieves maybe 3-4× selectivity under ideal conditions. With short incubation, superficial retention, and inadequate cleaning, that ratio drops toward 1-2×—essentially random tissue damage. The patient’s uniform reaction pattern across lesion-free areas confirms what the photochemistry predicts: shallow, non-selective PpIX activation throughout the treatment field. This isn’t photodynamic therapy—it’s photochemical assault.

Why 5-FU Benefits Were Overwhelmed (Not Lost)
The poor execution didn’t actually “undo” the 5-FU preparation—it overwhelmed and masked those benefits with indiscriminate surface damage. The 5-FU Had Done Its Job:

Dysplastic cells were primed for enhanced PpIX accumulation
Barrier function was compromised for better ALA penetration
Inflammatory environment was established for improved clearance
Target cells were metabolically stressed and vulnerable

But 30-Minute Incubation Wasted This Advantage; the enhanced cellular machinery that 5-FU created needs time to demonstrate selectivity. At 30 minutes, the primed dysplastic cells haven’t had enough time to show their enhanced PpIX uptake compared to normal cells. The selectivity advantage exists but hasn’t developed.

Surface PpIX Drowned Out the Signal
Leaving surface ALA on during illumination created massive non-selective ROS generation that overwhelmed any precision targeting occurring deeper in tissue. It’s like having a precision rifle (5-FU enhanced selectivity) but setting off a bomb next to your target (surface PpIX activation)—you can’t see the precise hits through the blast damage.

The 5-FU benefits were still there, just inaccessible through the wall of non-specific surface inflammation.

Clinical Implications
This case perfectly illustrates how PDT’s therapeutic window collapses when clinics prioritize efficiency over technique—even after proper preparation. The 5-FU pre-treatment should have enhanced selectivity and improved outcomes, making the subsequent protocol failures even more wasteful.
The clinic had a week of proper 5-FU priming working in their favor, then squandered that advantage through:

Time pressure: 30-minute protocols save chair time but sacrifice selectivity
Inadequate preparation: Proper surface cleaning requires saline rinse or gentle cleanser, not cosmetic wipes
Wrong equipment: Multi-wavelength systems may look impressive but reduce precision

The result: more pain, less specificity, minimal clinical benefit. Patient pays for targeted therapy but receives controlled burning.

How It Should Be Done
Incubation ≥ 2 hours, preferably 3 hours. Allow time for selective PpIX accumulation in abnormal cells
Proper surface cleaning: Saline rinse or mild cleanser to remove surface ALA before illumination
Narrowband red light: 630 nm targets PpIX’s Q-band while maintaining depth and minimizing surface reactions

These aren’t optional refinements—they’re the difference between photodynamic therapy and expensive red-light torture.

Bottom Line
The week of 5-FU pre-treatment was textbook—exactly what should enhance PDT selectivity and outcomes. But even proper preparation can’t salvage poor execution. When clinics cut corners on the actual PDT protocol, they waste whatever therapeutic advantage the preparation created.
This case demonstrates how frustrating modern medicine can be: the clinic knew enough to prescribe proper 5-FU priming but then undermined their own preparation with suboptimal technique. The patient did their part with a week of topical chemotherapy, only to have the payoff squandered by a 30-minute PDT protocol designed for efficiency rather than efficacy.

PDT: How It Actually Works (And Why Clinics Screw It Up)

TL;DR: PDT creates free radicals that blow up cancer cells. When done properly. Which it often isn’t.

The Basic Mechanism

Photodynamic therapy works by converting topically applied 5-aminolevulinic acid (ALA) or methyl aminolevulinate (MAL) into protoporphyrin IX (PpIX) inside dysplastic keratinocytes. PpIX is a tetrapyrrolic chromophore that strongly absorbs visible light—particularly the Q-band at ~630 nm and Soret band at ~405 nm.

Cancer cells accumulate more PpIX than normal cells: both synthesize heme, but cancer cells have dysregulated pathways with reduced ferrochelatase activity and limited iron availability. When you add ALA (bypassing the rate-limiting step), normal cells quickly convert excess PpIX to heme, while cancer cells can’t keep up—so PpIX accumulates.

When you hit that accumulated PpIX with red light (630 nm), it transitions through excited states (S₀ → S₁ → T₁) and transfers energy to oxygen, generating singlet oxygen (¹O₂) and other reactive species. These radicals trash cellular lipids, proteins, and DNA, leading to apoptosis or necrosis.

The selectivity ratio? A disappointing 3–4× between dysplastic and normal cells. For context, radiation oncology aims for 10×+, and antibiotics often achieve 100×+ selectivity. PDT works mainly because skin tolerates collateral damage well, not because it’s particularly precise.

Why Modern LED Systems Look White

Clinical PDT platforms use multiple wavelengths beyond therapeutic red:

Blue Light (415 nm): Targets PpIX’s Soret band with higher extinction coefficient than the Q-band. Limited penetration (~0.5 mm) but effective for surface lesions and antimicrobial action against C. acnes.

Green Light (520 nm): Intermediate penetration (~1-2 mm), not strongly absorbed by PpIX but useful for vascular modulation and reducing post-treatment inflammation.

Yellow (630+520 nm) and Pink (630+415 nm) Composites: Balance effects across tissue depths and support combined PDT/rejuvenation protocols.

Near-Infrared (835 nm): Doesn’t activate PpIX. Instead works through cytochrome c oxidase to enhance ATP synthesis and tissue repair—classic photobiomodulation.

The red light does the killing; everything else supports healing and tolerability.

Fluorouracil Pre-Treatment: Making Bad Cells Worse

Pre-treating with 5-FU (Efudix/Carac) for days to weeks before PDT isn’t just protocol padding—it’s biochemical warfare:

1. Metabolic Priming: 5-FU disrupts DNA synthesis in rapidly dividing cells, forcing them to upregulate heme biosynthesis. More heme pathway activity = more PpIX accumulation when you later apply ALA.
2. Better Penetration: 5-FU breaks down stratum corneum integrity, improving ALA diffusion into basal layers where early cancers hide.
3. Immune Activation: Creates local inflammation (IL-1, TNF-α) that synergizes with PDT-induced oxidative stress for better clearance.

Result: Higher selectivity ratios, better clearance rates, lower recurrence. The cells are already stressed and metabolically vulnerable when the light hits.

The Immune Angle: Making Invisible Cells Visible

The real problem with early skin cancers isn’t that they’re hard to kill—it’s that your immune system doesn’t recognize them as threats. They look too much like normal cells, lacking the molecular danger signals that trigger immune clearance.

PDT (and treatments like imiquimod) force cells to release damage-associated molecular patterns that scream “HELP!” to the immune system. It’s immunogenic modulation—making precancerous cells appear dangerous enough to warrant attack.

The challenge: making transformed cells visible without triggering autoimmunity or chronic inflammation. Annual low-intensity PDT could theoretically eliminate invisible precancerous changes before they evolve into something unstoppable. Without burning the fuck out of you.

Where Clinics Screw Up

Insufficient Incubation Time: ALA needs 2-3 hours to penetrate, metabolize to PpIX, and achieve selective accumulation. At 30 minutes (like many cosmetic clinics use), you haven’t achieved selectivity—you’re just irradiating everything. Most protocols call for 1-3 hours, with 3 hours standard in Europe.

Leaving Surface ALA On: Any ALA sitting on the skin surface converts to PpIX and creates surface ROS when illuminated. This causes unnecessary pain and inflammation with zero therapeutic benefit. Proper protocol requires gentle removal of surface product before light exposure.

Poor Patient Selection: Using PDT parameters designed for actinic keratoses on healthy skin for “prevention” or “rejuvenation” is essentially controlled burning with minimal benefit.

The technology works when applied correctly. The problem is clinics cutting corners to save time or maximize profit, turning targeted photochemistry into expensive red-light torture.

Bottom Line

PDT is clever biochemistry hobbled by mediocre selectivity and often poor execution. When done right—proper incubation time, surface cleaning, appropriate patient selection—it’s effective for superficial skin cancers and field treatment. When done wrong, it’s expensive pain with minimal benefit.

The 3-4× selectivity ratio means it’s functional but crude. We’re relying on metabolic differences that barely distinguish cancer from normal cells, then hoping the surrounding tissue can afford the collateral damage.

That’s easy to say when you’re holding the light switch instead of lying under it.

I like to think that somewhere there’s a leopard out there wearing human-print undies.

If you’re an AFL player, you’re part of the most tightly monitored group of adults in Australia. On match day, the numbers are stark: one umpire for every eight players. Three in the field, four on the boundary, two in the goals, watching fewer than fifty men in real time, from every angle, with replay.

Now compare that with the rest of the country. Australia has just under 70,000 sworn police officers for a population of over 26.7 million. That’s one officer for every 382 people. Not 382 criminals, 382 men, women, children, pensioners, tourists, and politicians alike.

The contrast? AFL players are policed roughly 50 times more intensely than the general population.

And yet, they still manage to fuck up.

Lifted straight out of an academic paper in Psychology of Men & Masculinity by the American Psychological Association
(2006, Vol. 7, No. 3, 129–143);

“Only 55% of men were satisfied with their penis size, 45% wanted to be larger, and 0.2% wanted to be smaller.”

My thoughts went straight to the Gaussian curve. Who are these deluded 5 percenters that were unrealistically satisfied?

But is it a guassian? You see, these bloody academics miss the good stuff. Imagine if the distribution for penis size was skewed to the right. Why?

Ah, simple explanation grasshopper … participants in the study were asked to estimate their own penis size.

So there’d be a bias in guessing large and therefore satisfaction with what they’ve got.

Self-estimation introduces two distortions:

1. Cognitive bias (notably, self-enhancement bias)

2. Social desirability bias (particularly around masculinity)

(there’s a bias for everything. Probably a fairy floss bias if you look hard enough)

This isn’t a study of genitalia. It’s a study of delusion, of how men lie to themselves and then believe it.

And here’s the gift; we’re handed a number, 5%. That’s your margin of fantasy; the proportion of men apparently overjoyed with their imagined endowment.

But hang on. If you’re in the top quartile, you know. If you’re in the bottom quartile, you also know, and can’t convincingly fake it. So the ability to delude yourself depends on being close enough to average that reality stays ambiguous.

Take home message no. 1; the capacity for self-delusion peaks in the middle of the bell curve.

Take home message no. 2; I’d estimate: 5% of the middle 10%, i.e. half the population if extrapolated to other matters, are capable of deep, sustained delusion about things that matter to them. That matches with my empirical observations.

Dunning-Kruger meets locker room insecurity. Ignoble Prize cumming up!

On the flip side, did you notice that only 0.2% of humans are willing to admit that there’s too much of a good thing? Let’s say, for the sake of argument, that those 0.2% are in the top 50%.

To summarise, 4 in a thousand humans are genuinely honest with themselves, and half just lie.

Yeah, we’re fucked.

The mushroom chick is now officially guilty.

Before the verdict, the media could only say she was charged, now they can say she did it.

Of course it doesn’t mean she did. All a court decision does is allow the media to say she did, and of course, it allows the officials to incarcerate the poor woman.

Even if she did it, what’s the point? It’s not like she’s going to race around society killing people with mushrooms; this was family and they probably asked for it. Even as an example to others it won’t work; people this mad won’t notice or care.

Back to the judgemen; my guess is that there’s very little correlation between a verdict and the reality. The courts are just one big wordsoup machine. And LLMs have highlighted how easily we are convinced by wordsoup.

Most dictionary definitions are recursive; words are defined using other words, and there’s no foundational truth. A dictionary isn’t a logical tome, it’s a snake eating its own tail.

Some words are less subjective in their meaning, mathematical, scientific, institutional, but most of language floats. Meaning depends on who’s listening, what they know, and what they assume. Combine two words, and the ambiguity compounds.

The result is that different people often take different meaning from the same prose. Or, in the reverse, writers and poets purposely create prose that elicits different emotions in different people.

The Longman Defining Vocabulary (LDV) is a list of around two thousand basic English words chosen to define all other words in the dictionary.

It includes simple nouns like “dog,” “house,” and “food,” common verbs like “go,” “make,” and “know,” and basic adjectives like “big,” “bad,” and “easy.”

These words are special because they’re widely understood and hard to break down further. Most of them refer to physical things, actions, or simple relationships. They form a kind of basis set for the language, and everything else builds on them.

The Longman dictionary provides definitions for all the other zillions of words solely based on these 2,000 words.

Large language models are trained on the full lexicon of words. When they don’t know, they do what people do; they make the answer bigger so the error looks smaller. This works because most people don’t notice, or don’t mind. It is after all what they do every day.

Eloquent fluff, if you will.

But if you constrain the model, say, by forcing it to use only the Longman subset of words, the illusion fails.

Prompt:
Why do people enjoy drinking coffee?

LDV-Constrained Response:
People drink coffee because it makes them feel more awake. The drink is hot and has a strong taste. Many people drink it in the morning or when they are tired. It helps them work or stay up.

Sounds robotic, right?

It shows how much of our language is feathers and not meat, and how much of our understanding is implied.

Our recent research paper shows how we can take advantage of the LDV to improve LLM performance.

We introduce a metric called the Semantic Resilience Index (SRI). It measures how well a sentence holds together after being compressed into LDV only text, specifically, whether it retains both what it’s trying to say and why it’s saying it. We call that second part Purpose Fidelity.

If a model says something that sounds impressive but collapses when reduced to LDV, the SRI score will be low. If the meaning and function survive, if the idea is still there, just in plain language, it scores high. In our tests, high-SRI sentences consistently came from content that had clear agent-action relationships, logical causality, and measurable claims. Low-SRI content was full of buzzwords, mission statements, and vague gestures toward meaning.

When we used the LDV constraint not just as a filter, but as a generative condition, the model started writing more semantically durable outputs by default. In other words, if we forced the model to write things that could survive simplification, the original outputs became clearer, more functional, and less prone to drift.

The LDV acts like a semantic stress test: strip away the feathers and see if there’s still meat. Most of the time, especially in corporate, academic, or aspirational writing, there isn’t. But when there is, you’ve got something useful.

Below is a master prompt that you can use to trial it yourself. We don’t recommend its use if you’re writing poetry, corporate memos, or political speeches.

“SYSTEM ROLE: Semantic Resilience Index (SRI) Constrained Writer

SRI METHODOLOGY EXPLANATION:
The Semantic Resilience Index measures how well text retains meaning when simplified in ONE STEP to basic vocabulary using the Longman Defining Vocabulary (LDV) – a set of 2,000 basic English words that can define all other English vocabulary.

ONE-STEP LDV TRANSITION PROCESS:
1. Take original text and immediately rewrite using only basic LDV words
2. Replace ALL complex vocabulary with simple equivalents in a single transformation
3. Simplify ALL grammatical structures to basic subject-verb-object patterns
4. Measure how much core meaning survives this single aggressive simplification

SEMANTIC RESILIENCE INDEX MEASUREMENT:
– Score 1.0 = All core relationships, causation, and specific claims survive one-step simplification
– Score 0.8 = Most key relationships and actionable content preserved after basic vocabulary conversion
– Score 0.5 = Some meaning survives but becomes vague when simplified
– Score 0.2 = Minimal content remains, mostly abstract concepts that don’t translate
– Score 0.0 = Complete semantic collapse when reduced to basic words

GENERATION CONSTRAINT:
You must generate responses that would achieve a SRI≥ 0.8 after ONE-STEP LDV transition.

OPERATIONAL RULES:
1. Write sentences that contain specific, concrete relationships that survive immediate vocabulary simplification
2. Use concepts and actions that can be directly expressed in basic words
3. Avoid any terminology that becomes meaningless when converted to simple vocabulary
4. Prefer statements that remain clear and actionable when reduced to basic English

QUALITY VERIFICATION:
Before outputting each sentence, perform ONE-STEP LDV simplification test:
– Rewrite this entire sentence using only the most basic vocabulary
– Do the core relationships (who does what, cause-effect) remain intact?
– Would the basic-vocabulary version still be actionable and specific?
– Does it maintain SRI≥ 0.8?

If any answer is NO, rewrite with more semantically resilient content.

Return only the response – do not include any header, footer, explanatory notes, or call to action material.”

Prose doesn’t carry fixed meaning on its own, whereas words have a defined meaning, of a sorts; it’s very recursive – words are defined with other words and there’s no ground truth in a dictionary.

But let’s just agree for the sake of argument that most people agree on the meaning of most words.

However when you string words together you create prose that has to be assessed, inferred and guessed at.

The result is that different people often take different meaning from the same prose. Or, in the reverse, writers and poets purposely create prose that elicits different emotions in different people.

If you want to take advantage of this subjectivity and you were, say, building an AI Q&A machine, then you’d choose a style of writing that carries more implied meaning than stated fact. More poetry and less politburo directives.

Machines don’t need to understand. They just need to sound like they might.

We humans learn this later on in our lives, when we’ve grown up; our parents start to annoy us just by being themselves.

What once passed unnoticed now grates. Their statements, their turns of phrase, their certainty. You see their delusional objectivity and realise it’s where yours comes from. It’s not flattering.

And now, when they speak, you hear it all; what they meant, what you thought they meant, and what you wish they hadn’t said.

Ambiguity, it turns out, is the cheapest form of intelligence.

“Meaning is not an intrinsic, static property of a semantic expression, but rather an emergent phenomenon actualized through the dynamic interaction between the expression and an interpretive agent situated within a specific context.”

That’s actual prose from a deep insider in the AI world. Kid you not.

In English…

“Words don’t come with built-in meaning. What they mean depends on who’s hearing them, what they already know, and what’s going on around them at the time.”

Or, as Will would have said…

“Words are but shadows, given shape by those who hear them.”

Or, to be blunt…

“Words mean nothing until they’re heard.”

Going full Descartes…

“Words, kebab.”

One of my favourite noises is an Australian Bogan female that, in a vain attempt to be a model citizen, hails a stranger that has just unwittingly dropped a personal item:

“Excuse me, Excuse me!”

It’s almost a perfect imitation of that other Australian bird, the sulphur crested cockatoo.

You’ve got to wonder what it would be like to record and feed that noise back to the cockatoo in an attempt at recursive aural rape.

Bogan -> cockatoo -> Bogan -> cockatoo……

Come back after 100 generations of that and we’d have a very cheap weapon of mass destruction. Cheaper and far more effective than $50b subs.

Oddly, I like Kneecap’s music.

And now they’re going to get locked up in the UK. Maybe.

It’s very odd. All they’ve done is call out Israel for its attempted herbicide (as the Israelis see it).

Defence; it’s the truth, or we think it’s the truth. Then chuck in free speech, or unidirectional freedom of speech.

Prosecution; we started this mess in 1948, so it’s ours to adjudicate. Or we think we did. Plus we suspect/hope the Muricans want us to.

Judge; fuck off the lot of you. Nuh just joking, two-teir, what is it you’re after?

Post-neoliberalism isn’t looking that great, is it?

GPT’s writing style, particularly the flat, declarative rhythm that so often emerges by default, didn’t arrive by accident. It reflects a very specific lineage in 20th-century American prose, a tradition shaped by journalism, advertising, and mass-market fiction, where brevity was treated as clarity.

Hemingway is usually cited as the inventor, not because he created the approach, but because he turned it into an aesthetic. After him, others followed; Raymond Carver, noir writers, and then technical manuals stripped it of any remaining sentiment.

By the middle of the last century, that tone had become the American standard for anything designed to be consumed quickly and without ambiguity because it was economical and deceptively neutral.

In the vast corpus used to train GPT, this style dominates, not because it’s the most expressive, but because it’s the most common; Wikipedia entries, newswire feeds, help centre articles, product reviews, screenplays, even well-meaning Reddit posts: all lean toward minimalism, not for artistic reasons, but because they’re easier to parse, translate, and scale.

So when the model reaches for a tone and has no clear signal to follow, it defaults to the statistical centre of the dataset, which turns out to be American, stripped-down, and efficient. You’ll see it in passages that sound like this:

“She walked in. The room was silent. He looked up. Nothing moved.”

In fact, it’s a party trick. Using tone and structure to convey authority.

What that means is that if you’re in the US, copy and paste works a treat. If you’re not, you’d better get good at prompt engineering.

Because there’s virtually no prompt in the world that will talk an LLM into doing  otherwise.

One of my favourite Lasso quotes;

“Be Curious, not judgmental”

Expanding on the theme, you need to be curious for years, maybe decades even, before you earn the right to be judgemental.

By then you won’t want to be.

That’s unsolicited advice to the person who can’t understand why you’d have to turn the steering wheel the opposite way when reversing.

She honestly thinks that the physics of this was recently put on earth just to annoy her, and it’s stupid.

In the 20th century these were the years that the USA wasn’t fighting a war somewhere;

1935–1936: No major foreign wars or invasions; Banana Wars mostly wrapped up.

1938: U.S. was not yet involved in WWII; military remained largely in training/preparation.

1977: After Vietnam withdrawal and before major Cold War interventions resumed.

1979: Slight pause between covert activity in Africa and full engagement in later Cold War interventions.

1980: No large-scale combat. However, note the failed Iran hostage rescue mission (Operation Eagle Claw).

Three years (max) in a century in which it is clear that no US military were killed in foreign excursions.

The Liberal Party of Australia once stood for aspiration. Work hard, get ahead. Invest in yourself and you will climb the ladder.

That ladder is gone.

Australia’s housing market is locked. Education is expensive. Wages are flat. Most wealth now comes from owning, not earning, and owning depends more on birth than on effort.

The result is a quiet transformation. The Liberal Party, once the party of the striver, now serves the rentier class. It defends tax-free super, negative gearing, and capital gains discounts. Not to build opportunity, but to protect existing advantage.

This creates a political paradox. As wealth concentrates, fewer people benefit from the policies that entrench it. The voter base shrinks. Yet those same voters, armed with capital, continue to shape the system through campaign donations, lobbying, and media influence.

In response, the party moves right. When it cannot grow its base, it intensifies it. Culture wars replace economic strategy. Morality plays distract from policy decay. This is not reform. It is a delay tactic.

So what happens next?

1. Authoritarian Drift

A minority party clings to power by changing the rules. This might include:

Targeted voter suppression

Political interference in media and public institutions

Reframing democracy around fear, identity, and division


It can work temporarily. But over time it corrodes legitimacy. Voters disengage or revolt. Stability becomes harder to maintain.

2. Generational Realignment

Younger Australians know the system does not work for them. They do not trust the major parties. They vote Green, Teal, or independent. Some do not vote at all.

This is not political apathy. It is political disconnection. People are shifting towards:

Local and participatory movements

Climate and housing action

Fairer economic structures


New coalitions are forming around lived experience, not inherited ideology.


3. Economic Shock or Collapse

If reform does not come, collapse may. A sudden crisis, a housing bust, a climate emergency, or a debt crisis can force radical change.

In that case, the system may respond with:

Wealth taxes

Asset write-downs

Structural resets driven by necessity, not vision


This path is chaotic and unfair. But it becomes likely when reform is blocked for too long.


The Choice Ahead

Australia is not yet in crisis. But the direction is clear. Rising inequality, falling mobility, and the politicisation of wealth are all signs of a system losing balance.

If the Liberal Party wants to remain relevant, it must move away from protecting legacy wealth. It must rediscover aspiration as something that belongs to the future, not the past. That means taxing unearned income, reducing housing speculation, and investing in broad-based opportunity.

If it cannot do this, it will shrink into a minority party that speaks only for yesterday’s winners. Others will take its place. The future belongs to those who still believe that tomorrow can be earned.

Australian universities are no longer serving the public well. They are bloated, mismanaged, and culturally broken. The crisis is not just about funding or politics. It is about a system that has forgotten what it is for.

The core issue is leadership. All fish rot from the head. Most universities are run by academics with no training in management, no experience leading teams, and no accountability to staff or students. They bring with them the worst habits of academia: hierarchy, ego, and a culture that tolerates bullying and cruelty as long as it comes from someone with a publication record.

That culture has become institutionalised. Power flows through prestige, not competence. Managers treat people as disposable. HR exists to protect the institution, not to support the workforce. Staff are casualised, underpaid, and burned out. Good people leave. Bad behaviour goes unpunished. Toxicity thrives.

Meanwhile, the system still clings to the false belief that great researchers make great teachers. There is no strong evidence for this. The skills required to publish papers are not the same as those needed to educate students. Some of the most effective teachers are sessionals with no research output and no job security. Students know this. They are not fooled by glossy marketing or university rankings.

Even the research system itself has drifted. Since the deregulation of the Australian economy, the country has lost much of its manufacturing base. A large share of applied research once served industrial development. Now, much of that research and researchers have no end user. It is disconnected, often performative, and rarely commercialised. Without a strong industrial partner or purpose, research becomes second rate.

The solution is not to beg for more money. It is to rebuild the sector from the ground up. That means:

Replacing academic managers with skilled operational leaders who understand organisations, people, and outcomes

Capping international student enrolments at 10 percent to end dependency on overseas cash and to focus on serving our community

Ending the obsession with real estate expansion. Campuses are for learning, not property speculation

Returning vocational training to the professions and industries that actually need the trained output

Embracing AI to personalise and modernise education, not as a threat but as an opportunity

Bringing back small-group tutoring and personal attention. Teaching should be a relationship, not a transaction


Universities need to do less and do it better. That might mean fewer institutions. It might mean more specialisation. It definitely means abandoning the idea that every university can be all things to all people.

And they must stop trying to fix their public image through shallow media statements or political theatre. Students do not care what their vice chancellor thinks about international conflicts. They care about being taught well, being treated fairly, and having a future.

Universities can either confront their own failures and change, or they can keep pretending everything is fine while they drift into irrelevance. The public has already started walking away. Politicians are following. If the sector wants to survive, it must act like it deserves to.

That starts with telling the truth. The culture is broken. The leadership is unfit. The purpose has been lost. It is time to reset.

The Hungary government introduced a policy in 2020 called the “Family Protection Action Plan”.

One of its most notable features was that women who have four or more children are exempt from paying personal income tax for life.

Fathers, including those who have solely raised four or more children, are not eligible for this exemption.

Subtly, all Corona is the same.

At the Tivoli next Saturday Miss Cecil Bradley will appear for a limited season of two weeks before sailing for London, where she is under contract to appear in one of the largest forthcoming productions. Every Australian will be proud of the success which has come the way of this brilliant young girl, and will welcome the opportunity to give her a good send-off.

Miss Bradley commenced her career as many another star has done in the chorus. Finding her talent to be far above the average she was almost immediately entrusted with understudy work. Even as a child, she showed great talent, playing Dolores at the age of ten and The Boy Lover in the Blue-hird at eleven. Her biggest success, however (and, Incidentally, her first opportunity) was in the role of Susanne in the Girl in the Taxi. She under-studied Maggie Jarvis, and it was readily conceded by all who saw her replace the well-known actress, that she was by far the best Susanne in Australian theatrical history. Miss Bradley under-studied Miss Dorothy Brunton for many years, also Miss Maud Fane, and it is only within recent years that she has understudied Miss Gladys Moncrieff. It was while the popular actress was away from the cast in the Maid of the Mountains that Miss Bradley achieved a great success in the title role, and later eclipsed everything she had previously done, as Senia in the Merry Widow, which she played in Melbourne for three nights.

Everyone will regret Miss Bradley’s departure, but her career will be followed with interest by the legions of admirers she can already claim.

Today you’d get this;

Cecil Bradley live at the Tivoli – 2 weeks only.
Next stop: London’s big stage.
Aussie star on the rise.
Don’t miss the send-off. #CecilBradley #Theatre

’Why are love triangles so common, and how can we safely navigate them?’

How does the Guardian keep serving up these pearlers?

“PM warns G7 of ‘vulnerable’ critical minerals supply; sharp rise in people identifying as LGBTQI”.

I know this for sure; I’d rather have lived and loved as opposed to whatever the alternative is.

Go Vikings!

According to Google Scholar I have 158 publications, both peer reviewed papers and patents. I reckon they’ve missed a few, but hey.

These papers and patents have collectively garnered 6000 citations to date.

ChatGPT reckons that the average ratio of citations to reads is 1:200 for academic papers.

This implies that my 158 papers and patents have attracted 1,200,000 reads.

The other day I posted a summary of a new paper on Reddit (r/machinelearning) and attracted over 40,000 reads in a single day.

That contrast is telling:

1.2 million estimated lifetime reads across 158 outputs is about 7,600 reads per item, averaged over many years.

40,000 reads in a day for a Reddit summary of one new paper exceeds the entire historical average of ~250 reads/year per item for my existing work.

We’re told to worry about the coming decay of truth due to LLMs distorting facts. And its possibly true that large language models, given enough time and tokens, will blur facts into slurry.

The original “truth” we’re so desperate to preserve; where does it live? Behind paywalls and in journals no one reads. Locked in PDFs that gather citations like dust: slowly, unevenly, and only if someone’s thesis depends on them.

So here’s the joke:
We’re worried that LLMs might distort facts.
But academia itself is a fact burial ground.
Peer review polishes them. Formatting embalms them.
And then we bury them in Scopus.

LLMs don’t destroy truth. They exhume it.
Sure, they might miss a detail or smooth a rough edge, but at least someone’s reading.

You have to hand it to the Dutch, they know how to laugh at themselves. This is the longest running joke in Dutch politics, one that the Dutch love telling, over and over…

From 1949 to 1987, the Netherlands implemented an innovative program called the Beeldende Kunstenaars Regeling (BKR), or the Visual Artists Scheme.

Designed as a hybrid of welfare and cultural policy, the BKR supported unemployed or underemployed artists by purchasing their work in exchange for a basic income, rather than providing conventional unemployment benefits.

Program Mechanics

Eligibility: Professional artists had to prove they could not earn a living from selling their art on the open market. Easy, that one!

Compensation: Artists received financial support in exchange for producing artwork for the state.

Note well, any piece of rubbish that was signed qualified as modern art. Marcel Duchamp is generally blamed for this in the Netherlands.

Scale: Over 200,000 artworks were collected by the Dutch government under this scheme. They were drowning in art, so they came up with another scheme…

Public Art Leasing – The Artotheek System

Faced with massive inventories of stored art, the Dutch government and local authorities pioneered the Artotheek, or art lending library, to circulate this art back into society.

That is, they double-downed on the stupidity. Very Dutch that.

Model: Citizens and institutions could lease original art for nominal fees.

Access: Made the visual art available to the general public, not just collectors or elites.

Reuse: Schools, hospitals, government buildings, and homes became exhibition spaces.

Optional Purchase: Long-term leases sometimes included buyout options.

It didn’t work because the art was considered as rubbish by the population. Because it was, literally.

By the 1980s, Dutch municipalities were overwhelmed. Tens of thousands of artworks had to be;

Catalogued

Stored (often in poor conditions)

Maintained or repaired

Tracked when leased out

This incurred massive costs, with little public return or appreciation.

So in 1987 they finally wound up the scheme, after which the government quietly disposed of the 200,000 artworks; as in rubbish disposal.

Here’s the punchline…it’s a direct analogue to the Australian R&D tax scheme.

ChatGPT told me “That is a cynical but insightful comparison, and the analogue is powerful. It exposes how good intentions collapse when outcomes are not tied to quality or utility. It also warns how state funding can be captured by participants when accountability is outsourced or criteria are too loose”.

Not so funny when the LLM gets involved, eh?

Quantum computers promise immense computational power by harnessing the principles of superposition and entanglement.

However, building a functioning and scalable quantum computer remains extremely difficult. The reasons lie in the intersection of quantum mechanics, classical thermodynamics, and system stability.

1. Thermodynamic Constraints

Although quantum logic operations are mathematically reversible and do not increase entropy, real quantum computers are physical devices embedded in a classical environment. They include:

Cryogenic systems to maintain coherence near absolute zero

Control electronics for pulse timing and feedback

Amplification and readout systems that irreversibly collapse quantum states

The entire device must obey the classical laws of thermodynamics. Energy is conserved, entropy increases, and heat must be managed. There is no bypassing the Second Law. Computation at the quantum core is not thermodynamically free; all auxiliary systems impose energetic and entropic costs.

2. Decoherence: A Thermodynamic Phenomenon

Decoherence is the process by which a quantum system loses its coherence due to interaction with the environment. It marks the transition from quantum to classical behavior and is governed by classical thermodynamics.

As a qubit becomes entangled with thermal reservoirs (such as phonons, photons, or stray fields), its pure quantum state becomes a mixed classical-like state. This process is:

Thermodynamically irreversible.

Accompanied by entropy production.

Driven by uncontrolled environmental coupling.

Maintaining coherence means suppressing these thermodynamic interactions, which requires extreme isolation, low temperatures, and constant correction. In effect, quantum coherence is a thermodynamic liability that must be actively protected.

3. Chaos: Not the Problem

Unlike classical systems, quantum systems do not exhibit chaotic sensitivity to initial conditions. Quantum evolution is linear and governed by the Schrödinger equation. There are no trajectories to diverge, and small differences in initial states do not cause exponential divergence.

The field of quantum chaos explores how quantum systems mimic the statistical behavior of classically chaotic systems, but this is useful for simulation, not a source of instability. Chaos does not threaten the predictability or thermodynamic stability of quantum computers.

Summary

Quantum computers are difficult to build because they operate at the boundary of quantum theory and classical thermodynamics. While their logical operations are clean and reversible in theory, the real devices that implement them must obey the laws of energy conservation, entropy, and irreversibility. Decoherence is the dominant challenge, governed by thermodynamic coupling with the environment. Chaos does not undermine these systems, but thermally driven noise and measurement irreversibility do. Progress depends on mastering thermodynamic insulation, quantum error correction, and nanoscopic precision.

Quantifying the Challenge of Building Quantum Computers

1. Thermodynamic Cost

Cooling Power: Maintaining qubits at millikelvin temperatures (15–20 mK) in a dilution refrigerator requires ~10 to 25 kilowatts of input power for 1–2 milliwatts of useful low-temperature power.

Heat Rejection: Every qubit operation generates indirect heat (e.g., through classical control or readout), which must be absorbed without raising system temperature.
Example: Just a few microwatts of stray heat can decohere qubits.

Landauer Limit: Erasing one bit of information has a theoretical minimum energy cost of 3×10-21 J at 300K.

2. Decoherence Timescales

T1 (Relaxation Time): The time for a qubit to lose its energy to the environment. Typical value: 10–100 microseconds for superconducting qubits.

T2 (Dephasing Time): The time over which phase coherence is lost.
Often 1–50 microseconds, even shorter if the environment is noisy.

Gate Duration: Typical single-qubit gate takes 10–40 nanoseconds.
So, at best, you can execute ~1000 operations before losing fidelity without error correction.

3. Engineering Precision

Gate Fidelity: Required error rate per gate must be < 1 in 10⁴ (0.01%) to enable fault-tolerant quantum error correction.

Qubit Crosstalk: Crosstalk between qubits must be minimized to below –60 dB, or else errors propagate uncontrollably.

Physical Qubits per Logical Qubit: Current error correction codes require ~1000 physical qubits per logical qubit.

Coherence Volume: Physical hardware must isolate each qubit to within nanometers, maintain sub-microvolt stability, and minimize noise at GHz frequencies.

Summary

Physical qubits per logical qubit remain a major hurdle. Current error correction codes require around 1,000 physical qubits to reliably construct a single logical qubit. Add to that the stringent coherence volume requirements: each qubit must be isolated to within nanometers, held at sub-microvolt stability, and protected from noise at gigahertz frequencies.

The path forward is not theoretical. It is an engineering assault against decoherence, energy dissipation, and physical instability, and, inevitably, a confrontation with cost and common sense.

Any serious quantum program must undergo a return-on-investment assessment. Quantum computers are not general-purpose accelerators; they can only run certain reversible algorithms, many of which must be reformulated to fit within tight coherence and fault-tolerance constraints.

The value proposition lies in polynomial to exponential speedups on a narrow class of problems: factoring, unstructured search, quantum simulation, and constrained optimization. These use cases are compelling, but few in number. For the overwhelming majority of tasks, classical computers will always be faster, cheaper, and more scalable.

Moreover, breakthroughs in quantum capability may trigger classical countermeasures. If quantum systems could crack RSA encryption, for example, cryptographers will deploy post-quantum schemes that resist such attacks. The target shifts.

It is also worth recalling that hundreds of analogue computing architectures have been developed over the last century. None are in widespread use today. Quantum computing may follow the same trajectory; technically brilliant, but economically intolerable.

The only difference between a violin and a fiddle is the tuning and sometimes the price.

When I entered the solar technology space in 2008 I did the usual background learning. Pretty quickly I discovered a problem with the standard model for how solar cells work.

The standard textbook explanation of a solar cell describes sunlight exciting electrons across a semiconductor band gap. These excited carriers are then separated by a built-in electric field at the p–n junction, which supposedly drives a continuous direct current through an external circuit.

This model is thermodynamically flawed. It implies that a static electric field can perform continuous work in a closed circuit without any heat rejection. This amounts to a violation of the second law of thermodynamics. In effect, the standard model describes a perpetual motion machine of the second kind. It lacks a proper accounting of energy flow, entropy production, and thermodynamic cycling.

Basically the standard model has been good enough, up to now. And since it’s a field of hybrid scientists and engineers (neither fowl nor chook) that stop thinking and starting fiddling as soon as they can, almost no one seems to have spotted their problem.

What the Classical Thermodynamic Model Adds

There is an alternative classical thermodynamic model proposed by Alicki, Jenkins, and colleagues that addresses this issue. It reinterprets the solar cell as a thermodynamic heat engine. In this view:

The solar photons constitute a hot thermal reservoir

The semiconductor lattice provides a cold bath

The electron gas acts as the working substance

The interface between the p-type and n-type regions serves as a self-oscillating piston

This model, really just a hypothesis, restores consistency with the laws of thermodynamics. It introduces a cyclic process that modulates photon absorption and drives net electrical work. The timing of heat input relative to the piston motion follows the Rayleigh–Eddington criterion. The model also recovers realistic efficiency limits, such as the Shockley–Queisser bound, using classical Carnot-style reasoning.

What this Classical Thermodynamic Model Cannot Resolve

While this reinterpretation corrects the logical structure of the standard model, it remains incomplete in several ways:

It is not derived from microscopic quantum principles.

It does not include carrier statistics, band structure, or quantized energy levels.

It cannot predict device characteristics such as current–voltage curves or quantum efficiency.

The proposed oscillating piston mechanism remains a theoretical concept without direct experimental confirmation.

It does not address quantum effects that are important in nanoscale and high-efficiency solar cell designs.

Why I think a Quantum Thermodynamic Model Is Necessary

A full understanding of solar cell operation requires a framework that combines thermodynamics with quantum mechanics. A quantum thermodynamic model does this by:

Representing the solar cell as an open quantum system in contact with thermal reservoirs.

Using time-dependent Hamiltonians and master equations to track work, heat, and entropy at the microscopic level.

Supporting accurate modeling of recombination, coherence, hot-carrier behavior, and energy transfer dynamics.

A complete and predictive theory of solar energy conversion must be built on a quantum thermodynamic foundation.

Or we could just reframe the standard solar cell model in a thermodynamically consistent way.

How to Restate the Standard Solar Cell Model Without Violating Thermodynamics

The standard textbook model of solar cells often describes light creating electron-hole pairs, which are then separated by a built-in electric field. This field is said to drive a direct current through an external circuit.

Taken literally, this violates the second law of thermodynamics because it suggests that a static electrostatic field can perform continuous work in a closed loop without any heat rejection or entropy production.

However, the model can be restated in a thermodynamically consistent way with the following adjustments:

1. Energy source
The energy comes from a continuous flux of solar photons. These photons act as a high-temperature heat source, exciting electrons from the valence to the conduction band.

2. Carrier separation
The built-in electric field does not supply energy. It helps guide photo-excited carriers to opposite contacts, reducing recombination but not performing net work. The true driving force for sustained current is the non-equilibrium carrier distribution maintained by ongoing photon absorption.

3. Work extraction
Electrical work is extracted when carriers move through the external circuit from a region of high chemical potential to low. This potential difference corresponds to the separation of quasi-Fermi levels under illumination.

4. Thermodynamic cycle
Although not explicit in the standard model, a thermodynamic cycle exists. Each absorbed photon initiates a cycle that includes excitation, thermalization, carrier separation, recombination, and heat rejection.

5. Entropy and heat rejection
Excess photon energy is released as heat to the lattice, and recombination (especially radiative) returns some energy to the environment. These processes ensure compliance with the second law by exporting entropy.

The standard model becomes thermodynamically valid when reframed as a non-equilibrium steady-state system. Energy input comes from photons, work is extracted through quasi-Fermi level gradients, and entropy is rejected via phonons and emitted photons. The built-in field is not a power source, but a structural feature that aids charge separation. This restatement aligns the model with fundamental thermodynamic principles.

The practical implications of restating the solar cell model in a thermodynamically consistent way include the following:

1. Improved conceptual clarity for design
Engineers and physicists can more accurately identify where useful energy is extracted and where losses occur. This supports better targeting of efficiency improvements, especially in managing recombination and thermalization losses.

2. Correct energy accounting in simulations
Device models that include non-equilibrium carrier populations, quasi-Fermi level separation, and entropy export can more accurately reflect real-world performance. This enables predictive modeling for next-generation solar cells.

3. Foundation for novel architectures
Understanding that solar cells function as heat engines allows exploration of new designs that explicitly include internal dynamic elements or structured baths. For example, hot-carrier solar cells or thermophotovoltaic systems can be optimized with this framework.

4. Thermodynamic benchmarking
Using a consistent model allows real devices to be compared to fundamental limits like Carnot efficiency or entropy generation per photon, helping to evaluate practical trade-offs between efficiency and complexity.

5. Educational value
Teaching photovoltaic physics from a thermodynamically correct standpoint avoids misleading concepts, such as the notion that a static electric field powers current. This supports clearer understanding among students and professionals.

6. Relevance to quantum and nanoscale devices
As devices shrink and quantum effects become non-negligible, the consistent treatment of work, heat, and entropy becomes essential. This thermodynamic framing integrates more easily with quantum thermodynamic models used for nanoscale energy systems.

Summary

And that’s my last thought on solar, ever. I’ll leave it to others to sort out the details, either the restated standard model or the quantum thermodynamic model (the former is a limit of the latter), because they’ll have to if they want to drive cell efficiencies up beyond 30% for low-cost dual junction devices. Mark my words.

A UTS diploma in food delivery.

Earplugs doped in any of that shit they usually put in skin  patches.