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Putin knows: if he uses nukes or strikes NATO, we will wipe Moscow off the map - Belgian Defence Minister Theo Francken told DeMorgen.
Putin warned of a tipping point if the West gave Ukraine long-range missiles. But what did he do? Nothing
The lesson: don’t be intimidated. 1/
Putin warned of a tipping point if the West gave Ukraine long-range missiles. But what did he do? Nothing
The lesson: don’t be intimidated. 1/
Francken: Putin made the same threats when Finland and Sweden joined NATO and when the West sent tanks and F-16s.
At first we only dared to fight back inside Ukraine, afraid of his reaction. That only prolonged the war. We must strike Russia’s supply lines. 2/
At first we only dared to fight back inside Ukraine, afraid of his reaction. That only prolonged the war. We must strike Russia’s supply lines. 2/
Francken: Russia remains a geopolitical great power with a strong army and fierce fighting spirit. They won in Chechnya, dominated Syria for a decade, and are active across Africa.
Europe lacks even a central command. Apart from Eurocorps in Strasbourg, we have nothing. 3/
Europe lacks even a central command. Apart from Eurocorps in Strasbourg, we have nothing. 3/
Here are full comments about LSU from Louisiana Gov. Jeff Landry at a news conference where he called Brian Kelly’s contract “terrible,” announced AD Scott Woodward would not hire the next coach and says he’d rather Donald Trump be in charge of the search than Woodward.
(thread)
(thread)
“My role is about the fiscal effect of firing a coach under a terrible contract. I care about what the tax payers' are on the hook on and I was not happy about raising ticket prices while we were having a losing season and we were paying a coach $100M and not getting results.”
“I think that it had gotten to the point that the spirit of the team needed a change and that change was made. We’re going to move on. We are without a president right now at LSU. I’m hoping the Board of Supervisors gets us one very quickly.”
Lets talk about...
The SS Empire Traveller, fuel, and Cherbourg in WWII
a back-story
The SS Empire Traveller, fuel, and Cherbourg in WWII
a back-story
June 6, 1944, D-Day, marked the Allied invasion of Normandy, but the real challenge was sustaining the advance. Fuel was critical—tanks, trucks, and planes guzzled it. Without steady supply, the liberation of Europe would stall.
After the beaches were secured, logistics was the next obvious challenge. Allied forces consumed over 800,000 gallons of fuel daily by late June. Initial supplies came via Mulberry harbours and beach offloading, but storms wrecked one Mulberry, exacerbating shortages.
It’s time to rebut this completely, embarrassingly wrong and hostile thread. Link to article and long 🧵 below. Sorry for the delay, but quality writing takes longer than LLM-style writing that is superficially smart but uncomprehending and error-ridden.
Here is the article version, which covers …
- People admitting my points about manufacturing GDP real value-added even while calling me wrong
- A blow-by-blow rebuttal of Sichuan’s very bad article
- Technical details previously omitted for brevity
rectifications.net/p/fractally-ri…
- People admitting my points about manufacturing GDP real value-added even while calling me wrong
- A blow-by-blow rebuttal of Sichuan’s very bad article
- Technical details previously omitted for brevity
rectifications.net/p/fractally-ri…
But first, before rebutting Sichuan Mala, I appreciate Brent Moulton, formerly head of the GDP calculation at the BEA, for clarifying that most people should not be citing real value-added. This was the central point of my article.
1/ Is Xi In Trouble?
2/ Beijing announced last week that 9 generals have been purged, including He Weidong, whom Xi personally promoted to Central Military Commission Vice Chair and #3 commander of the People’s Liberation Army. Predictably, that news has provided a peg for Xi skeptics among the China watchers community to repeat their storyline claiming that Xi is in trouble.
3/ The Wall Street Journal reported: “Xi’s latest crackdown has raised questions about his ability to root out graft and enforce loyalty in the People’s Liberation Army.” wsj.com/world/china/xi…
🧵 THE ERA OF “YIELDLESS GOLD” IS OVER. 💥🥇
1️⃣
For decades they mocked gold:
“Gold pays no interest.”
“Gold is dead money.”
“Only fools stack it.”
But that narrative just collapsed in China.
1️⃣
For decades they mocked gold:
“Gold pays no interest.”
“Gold is dead money.”
“Only fools stack it.”
But that narrative just collapsed in China.
2️⃣
ICBC, Everbright, and other Chinese banks now pay interest on gold accumulation accounts.
0.2% to 0.5% per year — not in paper money, but in gold grams.
That’s right: gold earning gold. ⚡
ICBC, Everbright, and other Chinese banks now pay interest on gold accumulation accounts.
0.2% to 0.5% per year — not in paper money, but in gold grams.
That’s right: gold earning gold. ⚡
3️⃣
Each account tracks holdings down to four decimal places — preparing the system for future gold-based settlements and digital gold units.
Call it “gold savings,” but in truth it’s a prototype of gold-backed payments.
Each account tracks holdings down to four decimal places — preparing the system for future gold-based settlements and digital gold units.
Call it “gold savings,” but in truth it’s a prototype of gold-backed payments.
🇺🇸 Fed Chair Jerome Powell just ended his press conference following the Fed cutting rates by 25 basis points to 3.75%-4.00%.
Powell's message: December is "not a foregone conclusion, far from it."
We listened to the entire thing so you don't have to and here's what you need to know.
A thread 🧵
Powell's message: December is "not a foregone conclusion, far from it."
We listened to the entire thing so you don't have to and here's what you need to know.
A thread 🧵
QT ENDING
Major shift: Quantitative tightening ends December 1st. The Fed will roll over all maturing Treasuries and reinvest MBS principal into T-bills.
Why? Money market indicators showed they've reached the right level of reserves - repo rates rising, more pressure on funding markets.
Major shift: Quantitative tightening ends December 1st. The Fed will roll over all maturing Treasuries and reinvest MBS principal into T-bills.
Why? Money market indicators showed they've reached the right level of reserves - repo rates rising, more pressure on funding markets.
THE LABOR MARKET PUZZLE
Powell explained the job market slowdown has two causes:
1. Supply side: Sharp drop in immigration + declining labor force participation = fewer workers entering the market
2. Demand side: Companies hiring less, announcing layoffs (often citing AI automation)
Result: Near-zero job creation when adjusted for overcounting
Powell explained the job market slowdown has two causes:
1. Supply side: Sharp drop in immigration + declining labor force participation = fewer workers entering the market
2. Demand side: Companies hiring less, announcing layoffs (often citing AI automation)
Result: Near-zero job creation when adjusted for overcounting
One the DCMA quality inspectors on my team worked at an EMALS contractor in Texas.
I can't say more than the Chinese tested their EMALS at subsystem level (unlike the USN) with the knowledge the four catapults needed to be independent of each other for operations,
EMALS🧵
1/5
I can't say more than the Chinese tested their EMALS at subsystem level (unlike the USN) with the knowledge the four catapults needed to be independent of each other for operations,
EMALS🧵
1/5
...based on how the USN f--ked up their EMALS design.
That is, when any single EMALS catapult on the Ford class goes down for any reason. They all can't be used.
2/5
That is, when any single EMALS catapult on the Ford class goes down for any reason. They all can't be used.
2/5
As strategypage dot com put it in 2019:
"EMALS proved less reliable than the older steam catapult, more labor intensive to operate, put more stress on launched aircraft than expected and due to a basic design flaw if one EMALS catapult becomes inoperable,
3/5
"EMALS proved less reliable than the older steam catapult, more labor intensive to operate, put more stress on launched aircraft than expected and due to a basic design flaw if one EMALS catapult becomes inoperable,
3/5
@nicholaswu12 your article in @politico is wrong,
@Tom_Suozzi represents #NY03 which includes a small portion of northeastern Queens, part of New York City.
Your description, “a suburban Long Island district,” is not accurate.
He can legitimately endorse in his district.
@Tom_Suozzi represents #NY03 which includes a small portion of northeastern Queens, part of New York City.
Your description, “a suburban Long Island district,” is not accurate.
He can legitimately endorse in his district.
@FaithWardwell @UnrollHelper
deepmind just published something wild 🤯
they built an AI that discovers its own reinforcement learning algorithms.
not hyperparameter tuning.
not tweaking existing methods.
discovering ENTIRELY NEW learning rules from scratch.
and the algorithms it found were better than what humans designed.
here's what they did:
• created a meta-learning system that searches the space of possible RL algorithms
• let it explore millions of algorithmic variants automatically
• tested each on diverse tasks and environments
• kept the ones that worked, evolved them further
• discovered novel algorithms that outperform state-of-the-art human designs like DQN and PPO
the system found learning rules humans never thought of. update mechanisms with weird combinations of terms that shouldn't work but do.
credit assignment strategies that violate conventional RL wisdom but perform better empirically.
the discovered algorithms generalize across different tasks. they're not overfit to one benchmark.
they work like principled learning rules should, and they're interpretable enough to understand WHY they work.
we are discovering the fundamental math of how agents should learn.
led by david silver (alphago, alphazero creator). published in nature. fully reproducible.
the meta breakthrough:
we now have AI systems that can improve the way AI systems learn.
the thing everyone theorized about? it's here.
they built an AI that discovers its own reinforcement learning algorithms.
not hyperparameter tuning.
not tweaking existing methods.
discovering ENTIRELY NEW learning rules from scratch.
and the algorithms it found were better than what humans designed.
here's what they did:
• created a meta-learning system that searches the space of possible RL algorithms
• let it explore millions of algorithmic variants automatically
• tested each on diverse tasks and environments
• kept the ones that worked, evolved them further
• discovered novel algorithms that outperform state-of-the-art human designs like DQN and PPO
the system found learning rules humans never thought of. update mechanisms with weird combinations of terms that shouldn't work but do.
credit assignment strategies that violate conventional RL wisdom but perform better empirically.
the discovered algorithms generalize across different tasks. they're not overfit to one benchmark.
they work like principled learning rules should, and they're interpretable enough to understand WHY they work.
we are discovering the fundamental math of how agents should learn.
led by david silver (alphago, alphazero creator). published in nature. fully reproducible.
the meta breakthrough:
we now have AI systems that can improve the way AI systems learn.
the thing everyone theorized about? it's here.
why this breaks everything:
RL progress has been bottlenecked by human intuition.
researchers have insights, try variations, publish.
it takes years to go from Q-learning to DQN to PPO.
now you just let the machine search directly.
millions of variants in weeks instead of decades of human research.
but here's the compounding part:
each better learning algorithm can be used to discover even better ones.
you get recursive improvement in the narrow domain of how AI learns.
humans took 30+ years to get from basic Q-learning to modern deep RL.
an automated system can explore that space and find non-obvious improvements humans would never stumble on.
this is how you get to superhuman algorithm design.
not by making humans smarter, but by removing humans from the discovery loop entirely.
when david silver's lab publishes in nature about "machines discovering learning algorithms for themselves," you pay attention. this is the bootstrap beginning.
paper:
nature.com/articles/s4158…
RL progress has been bottlenecked by human intuition.
researchers have insights, try variations, publish.
it takes years to go from Q-learning to DQN to PPO.
now you just let the machine search directly.
millions of variants in weeks instead of decades of human research.
but here's the compounding part:
each better learning algorithm can be used to discover even better ones.
you get recursive improvement in the narrow domain of how AI learns.
humans took 30+ years to get from basic Q-learning to modern deep RL.
an automated system can explore that space and find non-obvious improvements humans would never stumble on.
this is how you get to superhuman algorithm design.
not by making humans smarter, but by removing humans from the discovery loop entirely.
when david silver's lab publishes in nature about "machines discovering learning algorithms for themselves," you pay attention. this is the bootstrap beginning.
paper:
nature.com/articles/s4158…
TL;DR for normal people:
imagine you're teaching a robot to learn. humans spent decades figuring out the "best ways" to teach machines (called learning algorithms).
deepmind built an AI that invents its own teaching methods. and they work better than ours.
why it matters:
→ we don't wait for human breakthroughs anymore
→ AI searches millions of strategies we'd never think of → each better algorithm helps discover even better ones (compounding)
→ we're automating the process of making AI smarter
it's like having a student who figures out better ways to study, then uses those better methods to figure out even better ones, recursively.
the "AI improving AI" loop is here. published. working.
the next generation of breakthroughs in how machines learn might be designed entirely by machines.
imagine you're teaching a robot to learn. humans spent decades figuring out the "best ways" to teach machines (called learning algorithms).
deepmind built an AI that invents its own teaching methods. and they work better than ours.
why it matters:
→ we don't wait for human breakthroughs anymore
→ AI searches millions of strategies we'd never think of → each better algorithm helps discover even better ones (compounding)
→ we're automating the process of making AI smarter
it's like having a student who figures out better ways to study, then uses those better methods to figure out even better ones, recursively.
the "AI improving AI" loop is here. published. working.
the next generation of breakthroughs in how machines learn might be designed entirely by machines.
5. … they are ‘intending to transition’, worker B has transitioned.
A is not on a project because team leader TL shows A is bad at job.
B is not on project because TL says they don’t believe in ‘sex changes ‘ & they will misgender B.
A does not have a claim, B does. …
A is not on a project because team leader TL shows A is bad at job.
B is not on project because TL says they don’t believe in ‘sex changes ‘ & they will misgender B.
A does not have a claim, B does. …
6. ‘Intention to’ affords protection in some limited circumstances, but not where d&h is irrelevant to the intention.
The Act affords protection where d&h is directly related to the words in the Act, the Explanatory notes. & in Statutory Guidance
The inclusion of gender …
The Act affords protection where d&h is directly related to the words in the Act, the Explanatory notes. & in Statutory Guidance
The inclusion of gender …
7. … reassignment in the EqAct came about because of the relentless prejudice, d&h trans people experienced in all workplaces. & when accessing services etc
If we had been treated decently that wouldn’t have been necessary
@threadreaderapp unroll
If we had been treated decently that wouldn’t have been necessary
@threadreaderapp unroll
Merged 87 weak articles into 12 comprehensive guides.
Organic traffic increased 156% in 90 days.
No new content. Just strategic consolidation.
Here's the exact content merging framework: 🧵
Organic traffic increased 156% in 90 days.
No new content. Just strategic consolidation.
Here's the exact content merging framework: 🧵
1/ Why content consolidation works:
Google rewards depth over quantity.
Problems with thin content:
Keyword cannibalization (pages competing)
Diluted authority (weak links spread thin)
Poor user experience (scattered information)
Lower rankings (insufficient depth)
One strong page beats 10 weak ones.
Google rewards depth over quantity.
Problems with thin content:
Keyword cannibalization (pages competing)
Diluted authority (weak links spread thin)
Poor user experience (scattered information)
Lower rankings (insufficient depth)
One strong page beats 10 weak ones.
2/ The content consolidation audit process:
Step 1: Export all URLs and metrics
Use Screaming Frog + GSC to gather:
All published URLs
Organic traffic per page
Ranking keywords per page
Backlinks per page
Word count per page
Build a master spreadsheet with this data.
Step 1: Export all URLs and metrics
Use Screaming Frog + GSC to gather:
All published URLs
Organic traffic per page
Ranking keywords per page
Backlinks per page
Word count per page
Build a master spreadsheet with this data.
