McMurry recalls plenty of discussion about adhering to any 'real' physics if that event actually happened. "There was a very fun debate about what would happen if the center broke. Some people said, 'Well, these towers, since they're under stress, they would just go immediately back out this way.' We did some tests but then we said, 'Well, that looks stupid.'" [...]

"We were always scaling things up and down, cheating gravity stronger or weaker, just to make everything feel like it had the right scale but wasn't too slow to maintain the excitement. The destruction simulations were tweaked just to look good. They're not based on any mechanical analysis of material strengths."

"The bridge attack is one of the biggest cheats in the whole show," adds Knoll. "The nominal size for the Kaijus and Jaegers was around 250 feet tall, and we tried to stay around that where we could. The roadway of the Golden Gate Bridge is around 230 feet above the water, so the Kaiju in those shots is cheated up to around 700 feet tall!" [...]

Godzilla's interaction with the bridge -- despite ultimately breaking through it -- remains somewhat low-key. "In the movie, notes Bonami, "the cables are grabbed, shaken and cut. In reality if one of those cables were cut, the bridge would swing and if Godzilla were to walk thought it the effect on the bridge would be devastating. Our challenge was therefore to downplay the physics but also to try and maintain realism."

Previously, previously.

Calling for investigation of bets about the attack on Iran, because of suspicions the bettors had inside information, and the implications if they did.

Masood Masjoody, an Iranian dissident in exile in Canada, disappeared and it looks like the regime had him murdered.

*"They [the US and Israel] are also lying! Like the regime has been lying to us," said Amir, a student at the University of Tehran. "You are all worse than each other."*

It appears to Iranians that Israel and the US are attacking infrastructure rather that the regime's SS-equivalent, trying to weaken Iran for the long term rather than free Iran.

It doesn't surprise me that they were only trying to use the Iranians who have opposed the Iranian tyranny. Neither Netanyahu nor the bullshitter cares about human rights, democracy, or well being, not those of ordinary people in any country, and neither one hesitates to lie.

Even so, with even a lick of sense they would have tried carefully to protect those Iranians and keep them on our side. It would not have cost much.

Iran has plenty of supporters for its new supreme leader, just as it has plenty of protesters who hate him.

He is reported to hate the protesters and plans to ramp up the repression in Iran.

The wrecker threatens to go on strike by refusing to sign any bills that Congress might pass, unless/until it passes the voter-suppressing Subjugate America Act.

If he does go on strike, that would protect American from many bad bills that Republicans want to pass, bills which would attack our freedoms, crush the poor, or subject us to diseases.

Alas, we can't expect him to keep his word. He might at any moment break his own strike by signing some particularly vicious bill.

Interviews with civilians in Tehran who aren't sure which side of the war they need to fear more.

*The appointment of a controversial slate of [antivax] vaccine advisers by Robert F Kennedy Jr likely violated federal law, and all votes taken by the committee over the past year have been stayed, a federal judge ruled on Monday.*

In particular, their vote to remove many vaccines from the US childhood schedule has been rendered inactive — for now. But if a higher court reverses this court decision, that will reactivate the vote.

*These "advisers" "missed 99% of data" on Covid vaccines before making recommendation, memos reveal.*

A famous and celebrated Australian defender of human rights has condemned the government's endorsement of the US and Israeli attack on Iran.

"It's known by Minnesotans as a place where executives and CEOs live," Parr told 404 Media. "Famously Walter Mondale is from North Oaks, but also like United Healthcare executives and Target executives."

North Oaks has managed to largely stay unmapped on Street View because of the way the city handles its streets. In almost every city and town in the United States, property owners give an easement to their local government for the roads in front of their homes (or don't have any claim to the roads at all). In North Oaks, homeowners' property extends into the middle of the street, meaning there is literally no "public" property in the city, and the roads are maintained by the North Oaks Homeowners' Association (NOHOA): "the City owns no roads, land, or buildings." [...]

"Technically, if you launch your drone from public property, which anyone can do if you're a registered drone pilot, you can fly it straight up and above private property," Parr said. And so Parr stood at "six or seven different spots" directly outside the boundary of North Oaks and flew his drone around. "I just pulled my car over onto the shoulder and popped my drone up and flew it over," he added. [...]

"According to North Oak's ordinances, you can go like, visit a friend, or if you're a contractor working on a house, you can go into the city, but you have to be an invited guest," Parr said. "I made a Craigslist post asking for somebody to invite me and I got an absolute ton of responses."

Previously, previously, previously, previously, previously, previously, previously, previously.

"It's up to you to figure out what you're comfortable sharing back with Tinder," Tinder Head of Product Mark Kantor told 404 Media. Still, users can't pick individual photos they want analyzed or ignored. [...]

Tinder has already leaned heavily into AI. Kantor told 404 Media that artificial intelligence is writing more than half the app's code these days.

Previously, previously, previously, previously.

Afroman did not defame Ohio cops in a satirical music video that featured footage of them fruitlessly raiding the rapper's house, a jury found on Wednesday. [...]

The hip hop star wrote the satirical song "Lemon Pound Cake" and made a music video with real footage of the raid taken from his home surveillance cameras to raise money for property damage caused during the search, he has said.

Seven cops with the sheriff's office then sued him in March 2023, alleging the music video defamed them, invaded their constitutional privacy, and was an intentional infliction of emotional distress.

The video features footage of the cops busting down his door, and of one officer eyeing his "mama's lemon poundcake" with his gun drawn. [...]

An attorney for the police, meanwhile, demanded a total of $3.9 million in damages -- divided among the seven officers involved.

Previously, previously, previously, previously, previously, previously, previously, previously.

(satire) *[Deportation] Agent Injured After Repeatedly Trying To Detain People In Neighborhood Mural.*

US citizens: call on the Democratic National Committee to release their investigation into the causes of their defeat in the 2024 election.

Previously, previously, previously, previously, previously.

Previously, previously, previously, previously, previously, previously, previously, previously.

We’ve all heard of those network effect laws: the value of a network goes up with the square of the number of members. Or the cost of communication goes up with the square of the number of members, or maybe it was n log n, or something like that, depending how you arrange the members. Anyway doubling a team doesn't double its speed; there’s coordination overhead. Exactly how much overhead depends on how badly you botch the org design.

But there’s one rule of thumb that someone showed me decades ago, that has stuck with me ever since, because of how annoyingly true it is. The rule is annoying because it doesn’t seem like it should be true. There’s no theoretical basis for this claim that I’ve ever heard. And yet, every time I look for it, there it is.

Here we go:

Every layer of approval makes a process 10x slower

I know what you're thinking. Come on, 10x? That’s a lot. It’s unfathomable. Surely we’re exaggerating.

Nope.

Just to be clear, we're counting “wall clock time” here rather than effort. Almost all the extra time is spent sitting and waiting.

Look:

• Code a simple bug fix
  30 minutes

• Get it code reviewed by the peer next to you
  300 minutes → 5 hours → half a day

• Get a design doc approved by your architects team first
  50 hours → about a week

• Get it on some other team’s calendar to do all that
  (for example, if a customer requests a feature)
  500 hours → 12 weeks → one fiscal quarter

I wish I could tell you that the next step up — 10 quarters or about 2.5 years — was too crazy to contemplate, but no. That’s the life of an executive sitting above a medium-sized team; I bump into it all the time even at a relatively small company like Tailscale if I want to change product direction. (And execs sitting above large teams can’t actually do work of their own at all. That's another story.)

AI can’t fix this

First of all, this isn’t a post about AI, because AI’s direct impact on this problem is minimal. Okay, so Claude can code it in 3 minutes instead of 30? That’s super, Claude, great work.

Now you either get to spend 27 minutes reviewing the code yourself in a back-and-forth loop with the AI (this is actually kinda fun); or you save 27 minutes and submit unverified code to the code reviewer, who will still take 5 hours like before, but who will now be mad that you’re making them read the slop that you were too lazy to read yourself. Little of value was gained.

Now now, you say, that’s not the value of agentic coding. You don’t use an agent on a 30-minute fix. You use it on a monstrosity week-long project that you and Claude can now do in a couple of hours! Now we’re talking. Except no, because the monstrosity is so big that your reviewer will be extra mad that you didn’t read it yourself, and it’s too big to review in one chunk so you have to slice it into new bite-sized chunks, each with a 5-hour review cycle. And there’s no design doc so there’s no intentional architecture, so eventually someone’s going to push back on that and here we go with the design doc review meeting, and now your monstrosity week-long project that you did in two hours is... oh. A week, again.

I guess I could have called this post Systems Design 4 (or 5, or whatever I’m up to now, who knows, I’m writing this on a plane with no wifi) because yeah, you guessed it. It's Systems Design time again.

The only way to sustainably go faster is fewer reviews

It’s funny, everyone has been predicting the Singularity for decades now. The premise is we build systems that are so smart that they themselves can build the next system that is even smarter, that builds the next smarter one, and so on, and once we get that started, if they keep getting smarter faster enough, then the incremental time (t) to achieve a unit (u) of improvement goes to zero, so (u/t) goes to infinity and foom.

Anyway, I have never believed in this theory for the simple reason we outlined above: the majority of time needed to get anything done is not actually the time doing it. It’s wall clock time. Waiting. Latency.

And you can’t overcome latency with brute force.

I know you want to. I know many of you now work at companies where the business model kinda depends on doing exactly that.

Sorry.

But you can’t just not review things!

Ah, well, no, actually yeah. You really can’t.

There are now many people who have seen the symptom: the start of the pipeline (AI generated code) is so much faster, but all the subsequent stages (reviews) are too slow! And so they intuit the obvious solution: stop reviewing then!

The result might be slop, but if the slop is 100x cheaper, then it only needs to deliver 1% of the value per unit and it's still a fair trade. And if your value per unit is even a mere 2% of what it used to be, you’ve doubled your returns! Amazing.

There are some pretty dumb assumptions underlying that theory; you can imagine them for yourself. Suffice it to say that this produces what I will call the AI Developer’s Descent Into Madness:

1. Whoa, I produced this prototype so fast! I have super powers!

2. This prototype is getting buggy. I’ll tell the AI to fix the bugs.

3. Hmm, every change now causes as many new bugs as it fixes.

4. Aha! But if I have an AI agent also review the code, it can find its own bugs!

5. Wait, why am I personally passing data back and forth between agents

6. I need an agent framework

7. I can have my agent write an agent framework!

8. Return to step 1

It’s actually alarming how many friends and respected peers I’ve lost to this cycle already. Claude Code only got good maybe a few months ago, so this only recenlty started happening, so I assume they will emerge from the spiral eventually. I mean, I hope they will. We have no way of knowing.

Why we review

Anyway we know our symptom: the pipeline gets jammed up because of too much new code spewed into it at step 1. But what's the root cause of the clog? Why doesn’t the pipeline go faster?

I said above that this isn’t an article about AI. Clearly I’m failing at that so far, but let’s bring it back to humans. It goes back to the annoyingly true observation I started with: every layer of review is 10x slower. As a society, we know this. Maybe you haven't seen it before now. But trust me: people who do org design for a living know that layers are expensive... and they still do it.

As companies grow, they all end up with more and more layers of collaboration, review, and management. Why? Because otherwise mistakes get made, and mistakes are increasingly expensive at scale. The average value added by a new feature eventually becomes lower than the average value lost through the new bugs it causes. So, lacking a way to make features produce more value (wouldn't that be nice!), we try to at least reduce the damage.

The more checks and controls we put in place, the slower we go, but the more monotonically the quality increases. And isn’t that the basis of continuous improvement?

Well, sort of. Monotonically increasing quality is on the right track. But “more checks and controls” went off the rails. That’s only one way to improve quality, and it's a fraught one.

“Quality Assurance” reduces quality

I wrote a few years ago about W. E. Deming and the "new" philosophy around quality that he popularized in Japanese auto manufacturing. (Eventually U.S. auto manufacturers more or less got the idea. So far the software industry hasn’t.)

One of the effects he highlighted was the problem of a “QA” pass in a factory: build widgets, have an inspection/QA phase, reject widgets that fail QA. Of course, your inspectors probably miss some of the failures, so when in doubt, add a second QA phase after the first to catch the remaining ones, and so on.

In a simplistic mathematical model this seems to make sense. (For example, if every QA pass catches 90% of defects, then after two QA passes you’ve reduced the number of defects by 100x. How awesome is that?)

But in the reality of agentic humans, it’s not so simple. First of all, the incentives get weird. The second QA team basically serves to evaluate how well the first QA team is doing; if the first QA team keeps missing defects, fire them. Now, that second QA team has little incentive to produce that outcome for their friends. So maybe they don’t look too hard; after all, the first QA team missed the defect, it’s not unreasonable that we might miss it too.

Furthermore, the first QA team knows there is a second QA team to catch any defects; if I don’t work too hard today, surely the second team will pick up the slack. That's why they're there!

Also, the team making the widgets in the first place doesn’t check their work too carefully; that’s what the QA team is for! Why would I slow down the production of every widget by being careful, at a cost of say 20% more time, when there are only 10 defects in 100 and I can just eliminate them at the next step for only a 10% waste overhead? It only makes sense. Plus they'll fire me if I go 20% slower.

To say nothing of a whole engineering redesign to improve quality, that would be super expensive and we could be designing all new widgets instead.

Sound like any engineering departments you know?

Well, this isn’t the right time to rehash Deming, but suffice it to say, he was on to something. And his techniques worked. You get things like the famous Toyota Production System where they eliminated the QA phase entirely, but gave everybody an “oh crap, stop the line, I found a defect!” button.

Famously, US auto manufacturers tried to adopt the same system by installing the same “stop the line” buttons. Of course, nobody pushed those buttons. They were afraid of getting fired.

Trust

The basis of the Japanese system that worked, and the missing part of the American system that didn’t, is trust. Trust among individuals that your boss Really Truly Actually wants to know about every defect, and wants you to stop the line when you find one. Trust among managers that executives were serious about quality. Trust among executives that individuals, given a system that can work and has the right incentives, will produce quality work and spot their own defects, and push the stop button when they need to push it.

But, one more thing: trust that the system actually does work. So first you need a system that will work.

Fallibility

AI coders are fallible; they write bad code, often. In this way, they are just like human programmers.

Deming’s approach to manufacturing didn’t have any magic bullets. Alas, you can’t just follow his ten-step process and immediately get higher quality engineering. The secret is, you have to get your engineers to engineer higher quality into the whole system, from top to bottom, repeatedly. Continuously.

Every time something goes wrong, you have to ask, “How did this happen?” and then do a whole post-mortem and the Five Whys (or however many Whys are in fashion nowadays) and fix the underlying Root Causes so that it doesn’t happen again. “The coder did it wrong” is never a root cause, only a symptom. Why was it possible for the coder to get it wrong?

The job of a code reviewer isn't to review code. It's to figure out how to obsolete their code review comment, that whole class of comment, in all future cases, until you don't need their reviews at all anymore.

(Think of the people who first created "go fmt" and how many stupid code review comments about whitespace are gone forever. Now that's engineering.)

By the time your review catches a mistake, the mistake has already been made. The root cause happened already. You're too late.

Modularity

I wish I could tell you I had all the answers. Actually I don’t have much. If I did, I’d be first in line for the Singularity because it sounds kind of awesome.

I think we’re going to be stuck with these systems pipeline problems for a long time. Review pipelines — layers of QA — don’t work. Instead, they make you slower while hiding root causes. Hiding causes makes them harder to fix.

But, the call of AI coding is strong. That first, fast step in the pipeline is so fast! It really does feel like having super powers. I want more super powers. What are we going to do about it?

Maybe we finally have a compelling enough excuse to fix the 20 years of problems hidden by code review culture, and replace it with a real culture of quality.

I think the optimists have half of the right idea. Reducing review stages, even to an uncomfortable degree, is going to be needed. But you can’t just reduce review stages without something to replace them. That way lies the Ford Pinto or any recent Boeing aircraft.

The complete package, the table flip, was what Deming brought to manufacturing. You can’t half-adopt a “total quality” system. You need to eliminate the reviews and obsolete them, in one step.

How? You can fully adopt the new system, in small bites. What if some components of your system can be built the new way? Imagine an old-school U.S. auto manufacturer buying parts from Japanese suppliers; wow, these parts are so well made! Now I can start removing QA steps elsewhere because I can just assume the parts are going to work, and my job of "assemble a bigger widget from the parts" has a ton of its complexity removed.

I like this view. I’ve always liked small beautiful things, that’s my own bias. But, you can assemble big beautiful things from small beautiful things.

It’s a lot easier to build those individual beautiful things in small teams that trust each other, that know what quality looks like to them. They deliver their things to customer teams who can clearly explain what quality looks like to them. And on we go. Quality starts bottom-up, and spreads.

I think small startups are going to do really well in this new world, probably better than ever. Startups already have fewer layers of review just because they have fewer people. Some startups will figure out how to produce high quality components quickly; others won't and will fail. Quality by natural selection?

Bigger companies are gonna have a harder time, because their slow review systems are baked in, and deleting them would cause complete chaos.

But, it’s not just about company size. I think engineering teams at any company can get smaller, and have better defined interfaces between them.

Maybe you could have multiple teams inside a company competing to deliver the same component. Each one is just a few people and a few coding bots. Try it 100 ways and see who comes up with the best one. Again, quality by evolution. Code is cheap but good ideas are not. But now you can try out new ideas faster than ever.

Maybe we’ll see a new optimal point on the monoliths-microservices continuum. Microservices got a bad name because they were too micro; in the original terminology, a “micro” service was exactly the right size for a “two pizza team” to build and operate on their own. With AI, maybe it's one pizza and some tokens.

What’s fun is you can also use this new, faster coding to experiment with different module boundaries faster. Features are still hard for lots of reasons, but refactoring and automated integration testing are things the AIs excel at. Try splitting out a module you were afraid to split out before. Maybe it'll add some lines of code. But suddenly lines of code are cheap, compared to the coordination overhead of a bigger team maintaining both parts.

Every team has some monoliths that are a little too big, and too many layers of reviews. Maybe we won't get all the way to Singularity. But, we can engineer a much better world. Our problems are solvable.

It just takes trust.

Back in the heady days of macOS 14, you could customize the default reset-to environment of the "guest" user (Safari bookmarks, items on Dock, etc.) by copying stuff into "/System/Library/User Template/English.lproj/". And of course macOS 26 seems to have completely fucked this. How do you accomplish this now?

Previously, previously, previously, previously, previously, previously, previously.

Age Verification Lobbying: Dark Money, Model Legislation & Institutional Capture:

How corporate lobbying, think tank infrastructure, competing model legislation, and obscured funding networks are shaping age verification policy across 45 states and Congress.

This investigation documents a national lobbying operation spanning corporate spending, think tank infrastructure, dark money networks, and competing model legislation templates. Meta spent a record $26.3 million on federal lobbying in 2025, deployed 86+ lobbyists across 45 states, and covertly funded a group called the Digital Childhood Alliance (DCA) to advocate for the App Store Accountability Act (ASAA). But the operation extends beyond Meta. [...]

This investigation traced funding flows across five confirmed channels, analyzed $2.0 billion in dark money grants, searched 59,736 DAF recipients, parsed LD-2 filings, and mapped campaign contributions across four states to document the operation.

Previously, previously.