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Make America Healthy Again is now a corporation. A super PAC, three nonprofits, a trademark, a holding company. RFK Jr.'s own son sits on the commercial board. And the movement's founder may no longer run the machine that bears his name. 🧵
Two men built it. Tony Lyons, the publisher who prints RFK Jr.'s books. And Mark Gorton, the Wall Street quant who created LimeWire, the file-sharing app a federal court shut down for mass copyright infringement. Their money now underwrites the health movement.
Kennedy registered the MAHA trademark, earned $100,000 in licensing fees off it, then handed the mark to a Wyoming LLC run by Del Bigtree for no compensation. The slogan is a brand with a paid history. That is the tell.
'#ScreenWalkers':
#Earthsflat #satirecom about #quirky "#misfits" who can #step through (sufficiently large) #screens, hoping to #save #cinema (etc)??
#Earthsflat #satirecom about #quirky "#misfits" who can #step through (sufficiently large) #screens, hoping to #save #cinema (etc)??
they "clown around" in bad movies, which change accordingly??
most prefer to clown masked??
AI content generation changed in 2026.
It's no longer about generating hundreds of mediocre articles.
It's about generating hundreds of *excellent* articles without losing control.
Here’s a system that scales AI content while maintaining quality: 🧵👇
It's no longer about generating hundreds of mediocre articles.
It's about generating hundreds of *excellent* articles without losing control.
Here’s a system that scales AI content while maintaining quality: 🧵👇
1/ The AI content problem at enterprise scale:
Early approach (2024-2025):
"Let's use ChatGPT to write 100 blog posts."
Result: Generic, repetitive, barely passable.
2026 approach:
"Let's use AI to scale expert content that's tailored, original, and competitive."
Different mindset. Completely different results.
Early approach (2024-2025):
"Let's use ChatGPT to write 100 blog posts."
Result: Generic, repetitive, barely passable.
2026 approach:
"Let's use AI to scale expert content that's tailored, original, and competitive."
Different mindset. Completely different results.
2/ The three-layer AI content system:
Layer 1: Strategy + templates (human-driven)
Layer 2: AI generation (model-driven, within guardrails)
Layer 3: Human review + refinement (quality control)
Most teams skip Layer 1 + 3. That's why AI content sucks.
Layer 1: Strategy + templates (human-driven)
Layer 2: AI generation (model-driven, within guardrails)
Layer 3: Human review + refinement (quality control)
Most teams skip Layer 1 + 3. That's why AI content sucks.
🧵Newly declassified docs show journalist @ScottMStedman's secretive emails with the FBI.
FBI's Chris Pluhar says, "Scott, this is something that would remain between us, not appropriate to discuss with others.... Please don't discuss with new boss or others."
1/3 @SuspendablesUSA @KyleSeraphin @SteveFriend @SeniorChiefEXW
FBI's Chris Pluhar says, "Scott, this is something that would remain between us, not appropriate to discuss with others.... Please don't discuss with new boss or others."
1/3 @SuspendablesUSA @KyleSeraphin @SteveFriend @SeniorChiefEXW
2/3 In this email chain from August 13, 2018, Christopher Pluhar of @FBILosAngeles, congratulates Stedman in what appears to be an existing, familiar, operational relationship.
One wonders who "they" are.
Pluhar: "Scott, Congratulations are in order! Great job. I know you will do very well and they will be glad they got you. The Bureau can still be an option for you in the future so don't forget about that!
"Regarding the research we discussed with you at our last meeting, do you feel that you couldn't/shouldn't still pursue working some of those projects with us? If you don't feel like you can, no problem, but if you would still like to try to do some research in parallel, we can still give that a try. Either way is OK. Thanks!"
More.
One wonders who "they" are.
Pluhar: "Scott, Congratulations are in order! Great job. I know you will do very well and they will be glad they got you. The Bureau can still be an option for you in the future so don't forget about that!
"Regarding the research we discussed with you at our last meeting, do you feel that you couldn't/shouldn't still pursue working some of those projects with us? If you don't feel like you can, no problem, but if you would still like to try to do some research in parallel, we can still give that a try. Either way is OK. Thanks!"
More.
Stedman (popularly known as Fedman) replies - in a subject line titled "My future" -
"Thank you very much! I really appreciate that.
"I'll have to chat with my new boss about the research. I don't think it'll be a problem because it's not a full time gig, but I do want to clear it with my new higher ups to make sure there aren't any issues.
"Thanks again!"
Sixteen minutes later, FBI's Pluhar cautions Fedman:
"Scott, this is something that would remain between us, not appropriate to discuss with others. It would be something you would do on your own time, not related to your new job in any way. Please don't discuss with the new boss or others. We can chat more."
At 11:18, Fedman replies, "Understood. I'm in Joshua Tree with spotty service right now, but can we chat a bit later this week?"
FBI's Pluhar: "Yes, that works great, have fun in JT!"
More emails on the way?
"Thank you very much! I really appreciate that.
"I'll have to chat with my new boss about the research. I don't think it'll be a problem because it's not a full time gig, but I do want to clear it with my new higher ups to make sure there aren't any issues.
"Thanks again!"
Sixteen minutes later, FBI's Pluhar cautions Fedman:
"Scott, this is something that would remain between us, not appropriate to discuss with others. It would be something you would do on your own time, not related to your new job in any way. Please don't discuss with the new boss or others. We can chat more."
At 11:18, Fedman replies, "Understood. I'm in Joshua Tree with spotty service right now, but can we chat a bit later this week?"
FBI's Pluhar: "Yes, that works great, have fun in JT!"
More emails on the way?
Transformers struggle to generalize to tasks they were not explicitly trained on. Instead, we propose in 2026 that it is the job of the harness to generalize through composition.
We observe a powerful property when training RLMs: for tasks with shared structure that look different, the root model naturally learns the same trajectory, meaning it views the two task trajectories as the same! In other words, the Transformer does not need additional generalization capabilities to transfer capabilities from one task to the other, the harness induces it.
We find that well-designed harnesses form a quotient set over task trajectories, meaning their individual LLM calls can see structurally “similar” tasks as near-identical, token-for-token! Harnesses can effectively generalize for the Transformer during training, without relying on any intrinsic generalization capability from the model.
For example, RLMs can see problems of different lengths as the same: we show that RLMs can train exclusively on short tasks, and fully generalize to similar but unseen tasks 8-32x longer because it produces near identical trajectories for both.
Taking this further, we show that tasks across different domains (e.g. math solutions vs. essay writing) that share a decomposition strategy exhibit the same generalization effect. RLMs can train on the problem of finding which essays belong to the same author and improve performance on finding math problems that share similar solutions.
The full blogpost, experiments, and discussion are in the thread below.
We observe a powerful property when training RLMs: for tasks with shared structure that look different, the root model naturally learns the same trajectory, meaning it views the two task trajectories as the same! In other words, the Transformer does not need additional generalization capabilities to transfer capabilities from one task to the other, the harness induces it.
We find that well-designed harnesses form a quotient set over task trajectories, meaning their individual LLM calls can see structurally “similar” tasks as near-identical, token-for-token! Harnesses can effectively generalize for the Transformer during training, without relying on any intrinsic generalization capability from the model.
For example, RLMs can see problems of different lengths as the same: we show that RLMs can train exclusively on short tasks, and fully generalize to similar but unseen tasks 8-32x longer because it produces near identical trajectories for both.
Taking this further, we show that tasks across different domains (e.g. math solutions vs. essay writing) that share a decomposition strategy exhibit the same generalization effect. RLMs can train on the problem of finding which essays belong to the same author and improve performance on finding math problems that share similar solutions.
The full blogpost, experiments, and discussion are in the thread below.
Read full blog, condensed summary below: alexzhang13.github.io/blog/2026/harn…
We typically deal with applications involving an agent and an arbitrary environment. The layer between the state we observe and the action we take is the harness, which is a program over LMs.
The key desirable property of a harness is converting the state to an observation (i.e. trajectory) that is “locally in-distribution” (LID), meaning all individual LM calls see prompts they see as solvable / in-distribution with respect to their training data, even if the overall task is OOD.
Harnesses like Claude Code, Codex, ReAct, etc. naively append all observations, tool call outputs, and reasoning into context, which quickly compounds the main context. The main context thereby becomes out of distribution for the underlying neural network, and this is why so much effort is spent procuring huge and diverse agentic traces on environments we want to use our agents on.
A well-designed harness can reduce many of these similarly structured trajectories to the same token trace, enabling better generalization properties through straightforward scaling on environments.
The key desirable property of a harness is converting the state to an observation (i.e. trajectory) that is “locally in-distribution” (LID), meaning all individual LM calls see prompts they see as solvable / in-distribution with respect to their training data, even if the overall task is OOD.
Harnesses like Claude Code, Codex, ReAct, etc. naively append all observations, tool call outputs, and reasoning into context, which quickly compounds the main context. The main context thereby becomes out of distribution for the underlying neural network, and this is why so much effort is spent procuring huge and diverse agentic traces on environments we want to use our agents on.
A well-designed harness can reduce many of these similarly structured trajectories to the same token trace, enabling better generalization properties through straightforward scaling on environments.
McFaul: Trump’s strategy was to appease Putin by making concessions without demanding anything in return.
He ruled out NATO membership for Ukraine and halted military and economic aid. Putin gave him nothing. Trump now seems to realize appeasement does not work. 1/
He ruled out NATO membership for Ukraine and halted military and economic aid. Putin gave him nothing. Trump now seems to realize appeasement does not work. 1/
McFaul: Russia’s military has grown stronger, producing large numbers of drones and ballistic missiles.
The US and Europe, especially NATO’s eastern flank, must do more and coordinate joint defense commitments with Ukraine. 2/
The US and Europe, especially NATO’s eastern flank, must do more and coordinate joint defense commitments with Ukraine. 2/
McFaul: The West needs a Cold War–style strategy to deter Putin’s Russia. I once believed there was a clear divide between Putin’s dictatorship and Russian society.
I was wrong. Too many Russians support this war and accept the mass sacrifice of their own citizens. 3/
I was wrong. Too many Russians support this war and accept the mass sacrifice of their own citizens. 3/
BREAKING: A new Alzheimer's drug just did something no tau therapy has ever done in a randomized trial — cut tau in the brain by up to 65% AND slowed cognitive decline.
The trial is CELIA, run for 76 weeks.
But there's a twist that has Wall Street nervous. 🧵
The trial is CELIA, run for 76 weeks.
But there's a twist that has Wall Street nervous. 🧵
Most Alzheimer's drugs (like Leqembi) target amyloid plaques.
This one — diranersen (BIIB080) — targets tau, the OTHER hallmark protein that tangles up inside brain cells and tracks more closely with memory loss.
This one — diranersen (BIIB080) — targets tau, the OTHER hallmark protein that tangles up inside brain cells and tracks more closely with memory loss.
Instead of cleaning up tau after it builds up, Diranersen turns down production at the source.
It's an “antisense oligonucleotide” (ASO) — think of it as software that tells the cell “make less tau” before the mess even forms.
It's an “antisense oligonucleotide” (ASO) — think of it as software that tells the cell “make less tau” before the mess even forms.
Everyone don't bother messaging Donald he can't reply for a few days 😁😁😁😁😁😁😁😁😁😁😁😁😁😁
@SurreyRCMP @surreyps @LangleyRCMP
@ChrisPentecos @Donaldnnicm @LangleyResident @BarbaraDoduk @Cdnwatcher @Istandtoreason @facepalmchris @trustednerd @felixcruggins @CultureGuard @WaxMyBallsShow @FranLa9 @kfurneaux20 @VernThurston @JonYaniv @JNonsense46242
@ChrisPentecos @Donaldnnicm @LangleyResident @BarbaraDoduk @Cdnwatcher @Istandtoreason @facepalmchris @trustednerd @felixcruggins @CultureGuard @WaxMyBallsShow @FranLa9 @kfurneaux20 @VernThurston @JonYaniv @JNonsense46242
@SurreyRCMP @surreyps @LangleyRCMP @ChrisPentecos @Donaldnnicm @LangleyResident @BarbaraDoduk @Cdnwatcher @Istandtoreason @facepalmchris @trustednerd @felixcruggins @CultureGuard @WaxMyBallsShow @FranLa9 @kfurneaux20 @VernThurston @JonYaniv @JNonsense46242 @threadreaderapp unroll
10 books on the science of living longer:
1) Outlive by Peter Attia
( preventing chronic disease )
1) Outlive by Peter Attia
( preventing chronic disease )
2) Lifespan by David Sinclair
( reversing aging )
( reversing aging )
3) Why We Sleep by Matthew Walker
( sleep )
( sleep )
@CiarraDazed @Donaldnnicm LMFAO 😜👍👍
@CiarraDazed @Donaldnnicm @ChrisPentecos @Donaldnnicm @LangleyResident @BarbaraDoduk @Cdnwatcher @Istandtoreason @facepalmchris @trustednerd @felixcruggins @CultureGuard @WaxMyBallsShow @FranLa9 @kfurneaux20 @VernThurston @JonYaniv @JNonsense46242
@CiarraDazed @Donaldnnicm @ChrisPentecos @LangleyResident @BarbaraDoduk @Cdnwatcher @Istandtoreason @facepalmchris @trustednerd @felixcruggins @CultureGuard @WaxMyBallsShow @FranLa9 @kfurneaux20 @VernThurston @JonYaniv @JNonsense46242 @threadreaderapp unroll
Dr. Priyam here. Today, let me teach you how to interpret a Kidney Function Test (KFT) exactly how I do it as an Internal Medicine resident.
By the end, you won't have a single question left. And also KFTs aren't just about kidneys actually. Let me break it down.
By the end, you won't have a single question left. And also KFTs aren't just about kidneys actually. Let me break it down.
A standard KFT looks at:
1️⃣ Serum Creatinine
2️⃣ Blood Urea
3️⃣ Uric Acid
4️⃣ Electrolytes (Na+, K+)
Most people just look at the report and check if the numbers are marked high or low in red. But I look at the whole patient. Here is how.
1️⃣ Serum Creatinine
2️⃣ Blood Urea
3️⃣ Uric Acid
4️⃣ Electrolytes (Na+, K+)
Most people just look at the report and check if the numbers are marked high or low in red. But I look at the whole patient. Here is how.
Creatinine: The Filtration Marker
• It's a waste product of daily muscle breakdown. High Cr = Kidneys aren't filtering well.
• BUT context is key! A muscular gym-goer will have a higher baseline than a frail elderly patient. We treat the patient, not just the number.
• It's a waste product of daily muscle breakdown. High Cr = Kidneys aren't filtering well.
• BUT context is key! A muscular gym-goer will have a higher baseline than a frail elderly patient. We treat the patient, not just the number.
"Made in Palestine"—The Chocolate That Exposes the Anti-Israel Lie
Look at this vintage wrapper. A beloved Israeli chocolate brand, Elite (the one every Israeli kid grew up with), proudly stamped "Made in Palestine" decades before the modern State of Israel was reborn in 1948 ⬇️
Look at this vintage wrapper. A beloved Israeli chocolate brand, Elite (the one every Israeli kid grew up with), proudly stamped "Made in Palestine" decades before the modern State of Israel was reborn in 1948 ⬇️
⬇️ This isn't some gotcha for anti-Semites. This is history slapping political propaganda in the face. ⬇️
⬇️ The company was founded in 1934 under the British Mandate of Palestine—the name the international community used for the Jewish national homeland long before "Palestine" became a weaponized slogan. ⬇️
