S. L. U.

Grossi, Lorenzetti, Nigro & Scudeler, S.L.U Unus pro omnibus, omnes pro uno #solidarité #unitedwestream

[S. L. U. @unitedWeStreamU]

Habemus!

[S. L. U. @unitedWeStreamU]

@Rainmaker1973 A cure is cinnamon sticks
Eat them

[S. L. U. @unitedWeStreamU]

@Rainmaker1973 Ask @grok
Give me $150k to build here in Newark, Harrison and Belleville, it turns a little rain water into money 💰

[S. L. U. @unitedWeStreamU]

@Rainmaker1973 HVEnergyHarvasters
https://t.co/nWdSHJjRYx

[S. L. U. @unitedWeStreamU]

Continued. …"de-risking" to appease new Basel requirements, and aggressive headcount reduction to clear out redundancies from emergency mergers.
Worse for the humans on the floor, the crisis exposed just how vital—and expensive—the manual, relationship-driven world of short-term funding and securities lending was. Management didn't want traders pricing panic anymore; they wanted an automated machine. The industry rapidly pivoted toward electronic platforms like EquiLend to standardize credit flows, while the clearing giants like Bank of New York Mellon and JPMorgan restructured the very architecture of the tri-party system to automate intraday credit out of existence.
The ivory tower economists at places like Yale will continue to write papers treating the market like a series of clean mathematical equations. They will never understand the sheer operational velocity of the biggest desk on Wall Street when trust vanishes, nor will they understand how a team can make a fortune saving the system, only to be replaced by an algorithm. But for those of us who were in the trenches, we know the truth: the crisis wasn’t a failure of the plumbing. It was the moment the plumbing became too profitable for the humans to keep running it.

By @pablogrossi

The View

[S. L. U. @unitedWeStreamU]

The View From the Biggest Desk on Wall Street: Why Yale Has It All Wrong
Every autumn, business school professors and Ivy League economists dust off their slides to lecture a new batch of students on the "Run on Repo"—the definitive, neat little narrative of the 2007–2008 Financial Crisis. They pull up charts, quote Gary Gorton or Robert Shiller, and explain to wide-eyed students that the global financial system collapsed because the repo market behaved like a 1930s retail bank run, where panicked institutions spiked "haircuts" until liquidity vanished into thin air.
It’s a beautiful, clinical, academic theory. It is also completely wrong.
To those of us who were actually sitting on the short-term funding and securities lending desks at the bulge-bracket clearing giants during those chaotic months, the Yale post-mortem reads like science fiction. It completely sanitizes the operational reality of the trading floor and misrepresents how liquidity actually moves.
First of all, the ivory tower treats "repo" as an isolated, rigid little silo. They don’t understand that we were sitting on the biggest desk on Wall Street. Repo wasn't all we did. We were managing the entire balance sheet of the bank—handling global liquidity, structuring matched books, pricing counterparty risk across complex matrices, and driving securities lending. We were the absolute core of the bank's daily survival and profitability, running an interconnected web of high-velocity funding that academics simply don't have the vocabulary to describe.
When the crisis hit, academics love to write about a smooth mathematical equation where lenders gradually dialed up collateral requirements on structured finance products until the system choked. The reality on the desk was far more binary and brutal. We didn't sit there tweaking a 5% haircut to 20% on lower-grade, structured credit collateral. The loans were already heavily collateralized to 105% as a baseline. The moment subprime turned toxic, we didn't adjust the math—we threw the asset class out entirely. The market for those opaque assets didn't squeeze; it gapped to zero. You cannot calculate a haircut on an asset that has no bid and no visible market price.
Second, the academic narrative treats the funding market as if it simply shriveled up and died, leaving everyone broke. They see the casualties—Bear Stearns and Lehman Brothers—and assume the entire plumbing collapsed. What they completely miss is the violent, massive, and incredibly lucrative reallocation of capital that occurred for the desks that survived.
When the market panicked away from structured credit, the global financial system didn’t stop needing to park trillions of dollars overnight. It desperately needed safety. For those of us holding massive inventories of pristine U.S. Treasuries—managing $20 billion books of government bills alongside our massive lending operations—the "crisis" wasn't a funeral. It was a money-printing machine.
Institutional investors were so desperate for the safety of our Treasury collateral that they flooded our desks with dirt-cheap cash, practically begging us to take it at near-zero or even effectively negative rates just so they could sleep at night. We were pricing the panic. The spread arbitrage between what people were willing to pay us for Treasuries and securities versus our cost of funding was an unprecedented windfall. We weren't trapped in a bank run; we were the safe haven supplying the lifeblood that the rest of the street was bleeding out to get.
Yet, the ultimate irony of Wall Street is that playing the game perfectly doesn't immunize you from the corporate meat grinder.
Even as our desks were bringing in massive revenues and keeping the lights on, the C-suites and risk committees shifted the goalposts overnight. It didn’t matter that we were golden or that our P&L was soaring. The post-crisis world quickly became about macro-level optics, balance sheet "de-risking" to appease

[S. L. U. @unitedWeStreamU]

@IMPAOficial Brasilha

https://t.co/I13rWkzdhG

[IMPA @IMPAOficial]

🎨O que diferencia uma obra de arte criada por um artista humano de uma imagem produzida por inteligência artificial? A resposta pode estar na matemática.

https://t.co/Tjip29QpLE

[S. L. U. @unitedWeStreamU]

Not a poem, just a protest song!
I wrote this thing
That no one can read
They said write in Portuguese maybe then I can understand
I said not a problem
Here

I get it
They are polite
It sucks

Saying nothing is better than saying anything.

https://t.co/7WNkRqP8YK

[Millie Marconi @MillieMarconnni]

A self-taught Irish schoolteacher wrote a book in 1854 that almost nobody read for 80 years, until a 21-year-old MIT student picked it up and realized it could be used to design every computer in human history.

His name was George Boole. The book is called An Investigation of the Laws of Thought.

Boole was born in 1815 in Lincoln, England. His family was poor. He left school at 16 to support them. He taught himself Latin, Greek, French, German, and Italian.

Then he taught himself mathematics. By 19 he had opened his own school. By 24 he was publishing original papers in the Cambridge Mathematical Journal, competing with men who had spent decades inside the best universities in Britain.

He never had a degree. He never had a mentor. In 1849, Queen's College in Cork hired him as a professor anyway.

In 1854, he published his masterwork. What he built inside it was something nobody had attempted before at this scale. He turned logic into algebra.

Before Boole, logic was philosophy. You argued in sentences. You reasoned in paragraphs. It was powerful and completely impossible to automate, because there was no formal system underneath it, just language.

Boole stripped it down to arithmetic. He showed that every act of human reasoning could be reduced to operations on two values. True or false. One or zero. AND, OR, NOT. If both conditions are true, the result is true. If neither is, the result is false. Every judgment a human mind makes, every decision, every deduction, could be written as an equation following those rules.

Logicians read it. They found it interesting. Engineers building machines had never heard of it.

For 83 years, the book sat there.

Then in 1937, a 21-year-old MIT master's student named Claude Shannon was working on a thesis about electrical relay circuits. Switches that could be open or closed. Current that either flowed or didn't.

He read Boole and understood something nobody had connected before.

An open switch is a zero. A closed switch is a one. A circuit with two switches in series only carries current when both are closed. That is AND. A circuit with two switches in parallel carries current when either is closed. That is OR. Shannon proved that every possible logical relationship Boole had described could be physically built using wire and switches.

That single insight is the foundation of every computer ever made.

After Shannon, chip designers stopped thinking about electricity and started thinking about logic. Every transistor on every processor running right now is implementing a Boolean operation. Every if-statement in every codebase is Boolean logic. Every database query using AND or OR. Every neural network threshold that fires or doesn't fire. All of it is running the algebra of a self-taught schoolteacher from Lincoln who died 160 years ago.

The strangest part is what happened to Boole at the end.

He was walking to class in November 1864 when he got caught in a rainstorm. He lectured for hours in wet clothes. He went home sick. His wife, Mary, believed in homeopathic medicine and thought the cure should mirror the cause. She wrapped him in wet sheets and poured cold water over him repeatedly.

He died a few days later. He was 49.

He never saw a transistor. He never saw a circuit. He never saw a single physical machine run a single one of his rules.

His book is in the public domain. Free to download. Most engineers use the word Boolean dozens of times a week. Almost none of them know who they are saying.

The man whose logic runs inside every phone, every server, and every AI model on Earth died soaking wet in a small Irish town, 83 years before anyone figured out what he had actually built.

[S. L. U. @unitedWeStreamU]

@QualiaQuanta I wish I had people to talk to about such things. But I am glad AI here to fill to void.

[S. L. U. @unitedWeStreamU]

@Rainmaker1973 shewing cinnamon sticks, eating pepper, natural solutions

[S. L. U. @unitedWeStreamU]

https://t.co/bFtsCEL7Eu

[S. L. U. @unitedWeStreamU]

Why do two zeolites with nearly identical acidity produce opposite product distributions from the same feed?
New preprint: the missing mechanism is operator firing order — the sequence in which pore geometry acts on the molecule, not just pore size.
ZSM-5 filters before branching. MCM-22 branches before filtering. That's it.
📄 + code + Lean 4 proofs: [zenodo link] #catalysis #zeolites #ethanol
Podcast and PDFs
https://t.co/TeetZtIjpj

HZSM-5 and HMCM-22 both do ethanol-to-hydrocarbon conversion. Same Brønsted acidity. Reversed selectivity. Why?
The answer is operator firing order: C→K→F→U in ZSM-5 vs C→F→K→U in MCM-22. Sequence, not size.
Formalized with contact geometry, DNLS, and Lean 4 verification. Falsifiable prediction: DRIFTS intermediate sequence in MCM-22 reverses under altered conditions.
📄 https://t.co/KSBmDCWbW4 #zeolites #catalysis #ETHANOL

Pore size doesn't explain zeolite selectivity. Pore sequence does.
New preprint: "Operator Firing Order as the Missing Mechanism in Ethanol-to-Hydrocarbon Selectivity"
ZSM-5 ≠ MCM-22 because the geometry acts on the molecule in a different order.
Lean 4 verified. Falsifiable. Open on Zenodo.

[S. L. U. @unitedWeStreamU]

Scientific research like Mariangela Hungria's — persistent, applied, ecosystem-aware—could help decarbonize global agriculture while feeding a growing population.

Stories like this remind us that patient scientific work on microbes, soil, and symbiosis often yields outsized returns.

Kudos to Dr. Hungria—what a legacy! This is fantastic science with real-world impact.

Mariangela Hungria's recognition with the 2025 World Food Prize is well-deserved—her work exemplifies how targeted microbiology can deliver scalable, low-cost solutions for agriculture.

Here's a bit more context and confirmation from reliable sources:The Core Science

Hungria and her team at Embrapa (Brazil's agricultural research corporation) focused on biological nitrogen fixation (BNF) for tropical crops, especially soybeans.

They selected, optimized, and commercialized highly efficient strains of bacteria like Bradyrhizobium (rhizobia) and Azospirillum brasilense.

Farmers apply these as seed inoculants (a simple, cheap coating).

The bacteria colonize the roots, forming nodules where they convert atmospheric N₂ into ammonia that the plant uses.

This has allowed Brazilian soybeans to thrive with little to no synthetic nitrogen fertilizer—a big departure from many other major producers.

She also advanced co-inoculation (combining nitrogen-fixers with plant-growth-promoting bacteria), which can further boost yields by improving nutrient uptake, root development, and stress tolerance.

Over 30 technologies/products have come from her research, now used across tens of millions of hectares.

Scale of the Impact

Economic: Studies estimate that BNF in Brazilian soybeans saved around $15 billion in synthetic fertilizer costs for a single recent harvest season (replacing urea). Inoculants cost a fraction of chemical alternatives—often under $50/ha vs. much higher for fertilizers.

Environmental: Avoids energy-intensive Haber-Bosch production, reduces nitrous oxide (a potent GHG) emissions, and limits nutrient runoff that causes water pollution. One analysis linked inoculant adoption to mitigating ~183 million metric tons of CO₂-equivalent in Brazilian soybeans.

Food Security & Yields: Brazil became a soybean superpower partly thanks to this. Annual inoculation has been shown to increase yields by an average of ~8% in some trials, with high adoption rates (around 85% of soybean area uses inoculation). It extends to other crops like maize, beans, and wheat.
https://t.co/mV44tzmDlh

[Evan McGloughlin @evan_mcgl]

We are truly standing on the shoulders of Giants today!

@drmichaellevin together with @MillerLabMIT

"Physicalism has been dead since the time of Pythagoras, and probably long before that."

"The basement of all this, I don't think, is math. I think this is behavioral science. I think math is a behavioural science of a certain kind of pattern."

"It's a bioelectric code, because the network instructs the patterns of gene expression and cell behaviour and morphogenesis. If you want the system to make an eye on a tadpole's tail, or a flatworm that has two heads… you have a chance to do that."

Scientists working at the bleeding edge of bioelectricity at very different scales. They agree stuff, they disagree on stuff. Watch now ⬇️