Twilight View from Brooklyn by 1967 Millard Sheets

Twilight View from Brooklyn by 1967 Millard Sheets (America, 1907 - 1989)

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History

Renoir painted happiness... in the middle of war, hunger and chaos.

In 1870, the Franco-Prussian War began.

Renoir was called up to serve in the French Army.

In 1871, Paris descended into the Bloody Week.

Around 20,000 people were killed.

Food was scarce. The city was left… pic.twitter.com/eaXmiJeq2K

— Impressions (@impression_ists) July 4, 2026

 

An Insight

This is true. The climate mob erased the Medieval Warm Period because it did not fit the narrative. This is the key feature (i.e., fraud) in all hockey-stick graphs. https://t.co/BI5bKpsFOy

— John Shewchuk (@_ClimateCraze) July 11, 2026

 

I see wonderful things

So damn good. I’ve listened and watched this 10 times. You should too. pic.twitter.com/Arb42mEBqK

— Brian Wesbury (@wesbury) July 3, 2026

 

Offbeat Humor

Gentle reminder that almost everyone in this image was homeschooled. https://t.co/sxfteumkri pic.twitter.com/HACvt1R5CL

— The Culturist (@the_culturist_) July 3, 2026

 

Data Talks

Here's the biggest plot hole in the "genocide" narrative: we're expected to believe that the most technologically advanced military in the region, using the most powerful weapons in existence, dropped the equivalent of 8.6 nuclear bombs' worth of conventional explosives on one of…

— Jake Turx, Senior White House Correspondent (@JakeTurx) July 9, 2026

 

Incomplete knowledge, conflicting objectives among the different parties and binding constraints on who has the authority to make decisions

From What do consultants get paid for? by Luis Garicano.  The subheading is The analysis is the easy part.

A consultant I had lunch with recently is redesigning the loyalty program of a large airline. His team finished the analysis in two weeks. Months later the program still does not exist. This is because the purpose of the assignment is not to solve an analytical case study, but to figure out which redesign the parties will accept, and to get the people with authority to commit to implementing it.

I was not surprised to hear the story. In our just-published book Messy Jobs: The Work That AI Cannot Reach, Jin Li, Yanhui Wu, and I argue that a job is not a collection of independent tasks but a bundle of tasks and a position inside an organization. While many of the constituent tasks are clean, the job is messy because they must be combined under incomplete knowledge, conflicting objectives among the different parties and binding constraints on who has the authority to make decisions.

Hence we argue that automating the clean parts does not necessarily eliminate the job, because the remaining activities, tightly bundled with the rest, can remain the constraint. We argue that the bundle is strongest where separating the analytical/cognitive parts that can be automated would destroy local knowledge, trust, accountability or continuity.

This is related to Scott Adams' concept of a talent stack.  

Another point.

The real knowledge problem

An advocate of highly capable AI systems (“AGI-pilled”) would probably say this is a problem ready for AI. Have an agent redesign the program, have it meet the other constituencies, have it come back with a solution.

But what happens in those meetings deserves a closer look. There are four frictions in the room that make the meetings necessary.

First, knowledge is dispersed. This is the Hayek problem: different people hold different bits of information. AI can help with this. By making the emails, contracts, past redemption data and meeting transcripts searchable and available to the system, the model can extract part of this dispersed knowledge. But a lot of the dispersed knowledge remains in people’s heads as it is local and contingent, and will only emerge in the meeting.

Second, knowledge is also tacit: the Polanyi problem. We know more than we can tell. A senior partner in the consulting firm who knows the client well knows instinctively that a particular proposal will not fly. Again, AI may help reduce this problem, as it can learn from the actions people take on the basis of their knowledge. Brynjolfsson, Li and Raymond (2025) found that AI assistance diffused some of the communication and problem-solving practices of stronger customer-support agents to less experienced workers. The system had captured enough observable patterns in stronger agents’ behavior to reproduce some of their tacit knowledge advantage.

The third friction is that knowledge is not available to the AI system because people refuse to disclose it: strategic private information. The hotel knows how much it would cost to eliminate one feature of the loyalty program, and it will exaggerate that cost to extract more value in the exchange. Under specific assumptions about bilateral trade, Myerson and Satterthwaite showed that no mechanism can guarantee full efficiency while also inducing truthful revelation, respecting voluntary participation and balancing the budget. The theorem applies equally to humans and machines. Better models reduce the cost of drafting and bargaining, but don’t solve the problem of deciding who gets what.

The fourth friction is that the objective has not yet been formed or authorized. Think about the hotel chain. If you ask them initially what they want out of their program, they may not have an answer, since the organization does not know which features are critical or the cost of conceding a feature. That exploration only happens through iterative meetings, and what is happening in those meetings is that people are collectively discovering what the organization wants, what the different features are worth and which relationships they care about. The chief executive eventually makes the call, but the parts of the organization arrive at that point with different views and without a settled objective. The participants use those negotiations to discover the trade-offs, form their own view about their preferences among these trade-offs and authorize someone to bind the organization.

Letting “I dare not” wait upon “I would,” Like the poor cat i’ th’ adage?

From Folger Shakespeare, Macbeth Act1, Scene 7.

Synopsis:

Macbeth contemplates the reasons why it is a terrible thing to kill Duncan. Lady Macbeth mocks his fears and offers a plan for Duncan’s murder, which Macbeth accepts.

Hautboys. Torches. Enter a Sewer and divers Servants with dishes and service over the stage. Then enter Macbeth.

MACBETH 

If it were done when ’tis done, then ’twere well

It were done quickly. If th’ assassination

Could trammel up the consequence and catch

With his surcease success, that but this blow

Might be the be-all and the end-all here,

But here, upon this bank and shoal of time,

We’d jump the life to come. But in these cases

We still have judgment here, that we but teach

Bloody instructions, which, being taught, return

To plague th’ inventor. This even-handed justice

Commends th’ ingredience of our poisoned chalice

To our own lips. He’s here in double trust:

First, as I am his kinsman and his subject,

Strong both against the deed; then, as his host,

Who should against his murderer shut the door,

Not bear the knife myself. Besides, this Duncan

Hath borne his faculties so meek, hath been

So clear in his great office, that his virtues

Will plead like angels, trumpet-tongued, against

The deep damnation of his taking-off;

And pity, like a naked newborn babe

Striding the blast, or heaven’s cherubin horsed

Upon the sightless couriers of the air,

Shall blow the horrid deed in every eye,

That tears shall drown the wind. I have no spur

To prick the sides of my intent, but only

Vaulting ambition, which o’erleaps itself

And falls on th’ other—

Enter Lady Macbeth.

How now, what news?

LADY MACBETH 

He has almost supped. Why have you left the

chamber?

MACBETH 

Hath he asked for me?

LADY MACBETH  Know you not he has?

MACBETH 

We will proceed no further in this business.

He hath honored me of late, and I have bought

Golden opinions from all sorts of people,

Which would be worn now in their newest gloss,

Not cast aside so soon.

LADY MACBETH  Was the hope drunk

Wherein you dressed yourself? Hath it slept since?

And wakes it now, to look so green and pale

At what it did so freely? From this time

Such I account thy love. Art thou afeard

To be the same in thine own act and valor

As thou art in desire? Wouldst thou have that

Which thou esteem’st the ornament of life

And live a coward in thine own esteem,

Letting “I dare not” wait upon “I would,”

Like the poor cat i’ th’ adage?

MACBETH  Prithee, peace.

I dare do all that may become a man.

Who dares do more is none.

Jesmond, 2023 by Andrii Frolov

Jesmond, 2023 by Andrii Frolov (Ukraine, 1980 - )

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History

The “Sabu Disk” of ancient Egypt was discovered in 1936 inside a roughly 5,000-year-old tomb, and its purpose remains a mystery to archaeologists to this day.

The Sabu Disk is one of ancient Egypt’s most enigmatic artifacts. It was discovered in 1936 by British Egyptologist… pic.twitter.com/cUFobfb6Mi

— Historic Vids (@historyinmemes) July 4, 2026