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2. Cognitive Effects Nicotine improves cognition almost immediately. It boosts: • Working memory (UCLA, 2012) • Attention span (Nature, 1998) • Processing speed (Psychopharmacology, 2000) But how? Max Lugavere

Shayan Sen

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99,303 次观看 • 1 年前 •via X (Twitter)

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Shayan Sen1 年前

NICOTINE: The Most Lied-About Molecule in Medicine They let us believe it causes cancer. They didn't tell us that it enhances focus, improves metabolism, and protects your brain. Time to expose the real story and the science Big Pharma buried 🧵:

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Shayan Sen1 年前

1. Nicotine is not the same as Smoking Nicotine is not the villain. Smoke is. It’s a natural compound found in tomatoes, potatoes, and eggplant. Its structure closely resembles acetylcholine, your brain’s signal for calm focus. @hubermanlab

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Shayan Sen1 年前

Nicotine binds to receptors called nAChRs. These regulate: • Dopamine (motivation) • Glutamate (learning) • GABA (relaxation) • Serotonin (mood) This isn’t just an addictive molecule. It’s a fast-acting neural modulator.

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Shayan Sen1 年前

It amplifies signals in your prefrontal cortex. The part of the brain that handles focus, judgment, and working memory. It sharpens thought without overstimulation.

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Shayan Sen1 年前

3. Brain Protection Nicotine increases BDNF, a protein involved in memory, plasticity, and brain repair. BDNF: a protein involved in brain repair and learning

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Shayan Sen1 年前

Higher BDNF levels are linked to stronger long-term cognition. • 30% lower Parkinson’s risk (Annals of Neurology) • Delayed Alzheimer’s onset (J. Neurochemistry) • Fewer Tourette’s symptoms (NEJM) This isn’t fringe science. It’s peer-reviewed and reproducible.

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Shayan Sen1 年前

Nicotine also reduces brain inflammation by modulating microglia. Microglia are immune cells that clean up cellular damage. Less inflammation = slower cognitive decline.

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Shayan Sen1 年前

4. Metabolic Benefits Nicotine activates AMPK. It's an enzyme that tells your cells to burn fat and use energy more efficiently. It mimics fasting signals at the cellular level.

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Shayan Sen1 年前

The result: • Increased fat oxidation (Cell Metabolism) • Better insulin sensitivity (Diabetologia) • Lower visceral fat AMPK is the same pathway targeted by drugs like Metformin. @ThomasDeLauer

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Shayan Sen1 年前

5. Why You Never Heard This: Because you weren’t supposed to. 3 reasons: A. Guilt by association: Smoking harms but nicotine isn’t smoke. B. Regulatory bias: The FDA approves synthetic drugs (like varenicline) but denies benefits of natural nicotine. And finally..

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Shayan Sen1 年前

C. Pharma profits: Nicotine patches and gum generate over $1.3 billion per year. After the 1998 tobacco settlement, public health messaging was legally bound to portray all nicotine as harmful, regardless of delivery method. Even patches and gum.

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Shayan Sen1 年前

I wish someone had explained this to me 20 years ago. It would’ve changed how I saw caffeine, nicotine, even ADHD meds. We were told to fear nicotine. Then prescribed drugs that work the same way, but with more side effects.

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Shayan Sen1 年前

6. How to Use It Safely Nicotine isn’t harmless. But it can be used strategically. Clean delivery methods: • Patch • Gum • Sublingual tablets Avoid: • Cigarettes • High-dose vapes • Daily use without breaks Start low: 1–2 mg per session Use 2–4x per week Cycle to avoid tolerance Avoid if hypertensive, pregnant, or highly anxious. Not for anyone under 21. Nicotine can disrupt brain development and increase addiction risk in adolescents. Disclaimer: It's not for everyone. Do your research and make sure it's safe for you.

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Shayan Sen1 年前

The Bottom Line: Nicotine isn’t harmless. But it’s not what you were told either. Do your research. If this thread challenged what you’ve been told, it did its job. Follow @DrShayanSen for more evidence-based threads. Bookmark & share it if it made you think.

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Joe Rogan takes creatine every day, and says it’s one of the few things that can almost completely wipe out the mental effects of sleep deprivation. Gary Brecka backed it up: excellent for cognitive function, especially when sleep is off. If monohydrate causes bloating (common for women), switch to HCL. Pairing it with HMB improves uptake even more. Multiple studies (including a 2024 trial in Scientific Reports) show a high single dose of creatine significantly reduces cognitive decline during sleep deprivation, improving memory, processing speed, and vigilance by supporting brain energy stores. A simple daily supplement that helps protect your brain when life gets in the way of good sleep. What’s your experience with creatine, worth it or overhyped?

Joe Rogan takes creatine every day, and says it’s one of the few things that can almost completely wipe out the mental effects of sleep deprivation. Gary Brecka backed it up: excellent for cognitive function, especially when sleep is off. If monohydrate causes bloating (common for women), switch to HCL. Pairing it with HMB improves uptake even more. Multiple studies (including a 2024 trial in Scientific Reports) show a high single dose of creatine significantly reduces cognitive decline during sleep deprivation, improving memory, processing speed, and vigilance by supporting brain energy stores. A simple daily supplement that helps protect your brain when life gets in the way of good sleep. What’s your experience with creatine, worth it or overhyped?

Camus

79,454 次观看 • 3 个月前

Vigorous exercise improves cognition and delays brain aging. Exercise causes muscles to produce lactate, which travels to the brain, triggering the production of neurotransmitters like norepinephrine and neurotrophic factors like BDNF. New episode on why we should incorporate more vigorous exercise into our routine. TIMESTAMPS: (01:51) What differentiates "vigorous" from "zone 2" (09:12) Why zone 2 training may not improve VO2 max (11:34) Balancing zone 2 & vigorous-intensity workouts (13:32) Norwegian 4x4 protocol improves VO2 max (16:52) Methods to estimate VO2 max outside a lab (19:49) What it takes to reverse 20 years of heart aging (25:40) How HIIT reduces type 2 diabetes risk (26:54) HIIT vs. Zone 2 for mitochondria (29:25) Should committed exercisers practice 80/20 rule? (32:35) Vigorous exercise enhances mitophagy (35:20) Why lactate from HIIT benefits the brain (37:45) How lactate allows "glucose sparing" in brain (40:43) The unique BDNF benefits of HIIT (41:59) Lactate signals the growth of new blood vessels (44:15) Exercise intensity increases myokine release (47:04) Exercise lowers mortality risk in cancer survivors (48:13) Vigorous exercise kills circulating tumor cells (49:53) Why exercise reduces depression and neurotoxicity (kynurenine mechanism) (51:58) How "exercise snacks" reduce mortality Get the 9-page companion guide "The Cognitive Enhancement Blueprint" at

Vigorous exercise improves cognition and delays brain aging. Exercise causes muscles to produce lactate, which travels to the brain, triggering the production of neurotransmitters like norepinephrine and neurotrophic factors like BDNF. New episode on why we should incorporate more vigorous exercise into our routine. TIMESTAMPS: (01:51) What differentiates "vigorous" from "zone 2" (09:12) Why zone 2 training may not improve VO2 max (11:34) Balancing zone 2 & vigorous-intensity workouts (13:32) Norwegian 4x4 protocol improves VO2 max (16:52) Methods to estimate VO2 max outside a lab (19:49) What it takes to reverse 20 years of heart aging (25:40) How HIIT reduces type 2 diabetes risk (26:54) HIIT vs. Zone 2 for mitochondria (29:25) Should committed exercisers practice 80/20 rule? (32:35) Vigorous exercise enhances mitophagy (35:20) Why lactate from HIIT benefits the brain (37:45) How lactate allows "glucose sparing" in brain (40:43) The unique BDNF benefits of HIIT (41:59) Lactate signals the growth of new blood vessels (44:15) Exercise intensity increases myokine release (47:04) Exercise lowers mortality risk in cancer survivors (48:13) Vigorous exercise kills circulating tumor cells (49:53) Why exercise reduces depression and neurotoxicity (kynurenine mechanism) (51:58) How "exercise snacks" reduce mortality Get the 9-page companion guide "The Cognitive Enhancement Blueprint" at

Dr. Rhonda Patrick

531,276 次观看 • 2 年前

Dave Asprey claims you can boost your IQ by around 12 points in a month with one specific brain training method. On The Iced Coffee Hour, he described dual n-back training: a frustrating grid game where you track both the position and the symbol from several moves back. Just 20 minutes a day. Most people quit, but sticking with it for ~20 sessions can dramatically improve working memory (e.g., going from remembering 7–8 items to 16). Dual n-back training reliably improves performance on the trained task and can enhance working memory capacity. Some studies show modest transfer to fluid intelligence (problem-solving aspect of IQ), but results are mixed, meta-analyses find small average gains (often 2–5 points) that are stronger in lower-quality studies and don’t always hold up in well-controlled trials. The idea that you can deliberately train a core cognitive skill like working memory is intriguing, even if the IQ boost isn’t guaranteed to be that large. In a world full of distractions that shrink our attention, a simple daily practice that strengthens your brain’s ability to hold and manipulate information could be genuinely useful. Would you commit to 20 minutes of frustrating brain training every day for a month if it could meaningfully improve your focus and memory?

Dave Asprey claims you can boost your IQ by around 12 points in a month with one specific brain training method. On The Iced Coffee Hour, he described dual n-back training: a frustrating grid game where you track both the position and the symbol from several moves back. Just 20 minutes a day. Most people quit, but sticking with it for ~20 sessions can dramatically improve working memory (e.g., going from remembering 7–8 items to 16). Dual n-back training reliably improves performance on the trained task and can enhance working memory capacity. Some studies show modest transfer to fluid intelligence (problem-solving aspect of IQ), but results are mixed, meta-analyses find small average gains (often 2–5 points) that are stronger in lower-quality studies and don’t always hold up in well-controlled trials. The idea that you can deliberately train a core cognitive skill like working memory is intriguing, even if the IQ boost isn’t guaranteed to be that large. In a world full of distractions that shrink our attention, a simple daily practice that strengthens your brain’s ability to hold and manipulate information could be genuinely useful. Would you commit to 20 minutes of frustrating brain training every day for a month if it could meaningfully improve your focus and memory?

Camus

131,624 次观看 • 3 个月前

A video showing impact of max frequency change on my 2 x DGX Spark cluster with DeepSeek V4 Flash 0731 nvfp4 running like crazy at c4 using Mia recipe! Power consumption at wall is surely higher than the one reported by nvtop, but it gives you an idea: - 2455 MHz ~ 47W ~72º C - 2300 MHz ~34W ~67º C - 2200 MHz ~32W ~66º C - 2000 MHz ~27W ~63º C - 1800 MHz ~23W ~61º C This has minimal impact on decode speed that is memory bound, but there is impact on prefill and other compute bound activities. set it with: sudo nvidia-smi -lgc 0,2200 reset it with: sudo nvidia-smi -rgc I need to thank Paul to let me discover this magical trick!

A video showing impact of max frequency change on my 2 x DGX Spark cluster with DeepSeek V4 Flash 0731 nvfp4 running like crazy at c4 using Mia recipe! Power consumption at wall is surely higher than the one reported by nvtop, but it gives you an idea: - 2455 MHz ~ 47W ~72º C - 2300 MHz ~34W ~67º C - 2200 MHz ~32W ~66º C - 2000 MHz ~27W ~63º C - 1800 MHz ~23W ~61º C This has minimal impact on decode speed that is memory bound, but there is impact on prefill and other compute bound activities. set it with: sudo nvidia-smi -lgc 0,2200 reset it with: sudo nvidia-smi -rgc I need to thank Paul to let me discover this magical trick!

Ivan Fioravanti ᯅ

76,968 次观看 • 1 个月前

A new way of working. And a scary one at that. Memory Store is one of a group of new kinds of AI-first companies that can turn you into a Fast Company. I’m using several of them on my desktop and they are a dramatically new way to work. It builds a memory for: 1. Your AI agents. 2. Any employee using it. 3. The company itself. I sit down with founder Diwank Singh Tomer, Diwank Singh Tomer, who both freaks me out as well as shows how AI can radically help workers as well as managers. First, why does it freak me out? Well, his AI watches nearly everything a worker does and keeps a “memory” of it. It watches your email. Your calendar. Your Slack. And a whole lot of other things. This can really freak out workers if “forced” on them. And leads to a whole new set of security issues companies need to consider before adopting these things. Such data about a company could give a competitor a HUGE advantage, if leaked. They would know how a company “thinks.” It really is a surveillance system for employees and the company itself. OK, now why would anyone ever use such a thing? Because it gives employees super powers. It makes them more productive. Shows workers a lot of things about themselves, and helps them work and stay on task. It also gives the company super powers. Institutional memory stays with the AI now, even if an employee dies or leaves. As companies move to “AI First” approaches, they will increasingly see the value in companies like Memory Store. It prepares employees for meetings. It helps them remember things. It shows them what they should be working on, and helps them do it. Memory Store builds a memory for: 1. Your agents. 2. Your company. 3. Yourself, or any employee on it. This helps all three work better together. Diwank Singh Tomer and I go in depth about what it does and how deeply it improves working at a company that deploys it. But to get the ultimate benefits you gotta convince your coworkers to use it. And your managers to approve it. Which means you have to get over your fears and get everyone you work with over theirs too. Which will be the challenge for Diwank. Luckily for him his first customers are raving about how good it is and how much his platform helped their companies. Increases sales. Makes teams more productive. Decreases errors and unnecessary costs. Which tells me everyone soon will be using systems like this. This is what the new way of working looks like. Once I got over my fears it sure is an amazing way to work. Will you try working this way?

A new way of working. And a scary one at that. Memory Store is one of a group of new kinds of AI-first companies that can turn you into a Fast Company. I’m using several of them on my desktop and they are a dramatically new way to work. It builds a memory for: 1. Your AI agents. 2. Any employee using it. 3. The company itself. I sit down with founder Diwank Singh Tomer, Diwank Singh Tomer, who both freaks me out as well as shows how AI can radically help workers as well as managers. First, why does it freak me out? Well, his AI watches nearly everything a worker does and keeps a “memory” of it. It watches your email. Your calendar. Your Slack. And a whole lot of other things. This can really freak out workers if “forced” on them. And leads to a whole new set of security issues companies need to consider before adopting these things. Such data about a company could give a competitor a HUGE advantage, if leaked. They would know how a company “thinks.” It really is a surveillance system for employees and the company itself. OK, now why would anyone ever use such a thing? Because it gives employees super powers. It makes them more productive. Shows workers a lot of things about themselves, and helps them work and stay on task. It also gives the company super powers. Institutional memory stays with the AI now, even if an employee dies or leaves. As companies move to “AI First” approaches, they will increasingly see the value in companies like Memory Store. It prepares employees for meetings. It helps them remember things. It shows them what they should be working on, and helps them do it. Memory Store builds a memory for: 1. Your agents. 2. Your company. 3. Yourself, or any employee on it. This helps all three work better together. Diwank Singh Tomer and I go in depth about what it does and how deeply it improves working at a company that deploys it. But to get the ultimate benefits you gotta convince your coworkers to use it. And your managers to approve it. Which means you have to get over your fears and get everyone you work with over theirs too. Which will be the challenge for Diwank. Luckily for him his first customers are raving about how good it is and how much his platform helped their companies. Increases sales. Makes teams more productive. Decreases errors and unnecessary costs. Which tells me everyone soon will be using systems like this. This is what the new way of working looks like. Once I got over my fears it sure is an amazing way to work. Will you try working this way?

Robert Scoble

26,186 次观看 • 4 个月前

Dr. Jared Cooney Horvath just delivered the brutal truth parents and educators need to face: “Even in schools, it doesn’t matter what the size of the screen is… and it doesn’t matter who bought it… All of these things are going to hurt learning, which in turn are going to hurt our kids’ cognitive development.” His core warning: Gen Z is the first modern generation to be less cognitively capable than their parents — despite more years in school. Attention, memory, literacy, numeracy, executive function, even general IQ — all declining. The culprit isn’t school itself. It’s the widespread introduction of screens and digital tools for learning. Across 80 countries, once tech floods classrooms, performance drops sharply. Kids using computers ~5 hours/day for schoolwork score over 2/3 of a standard deviation lower than those who rarely touch tech. US NAEP data mirrors it: states adopt 1:1 devices → scores plateau, then fall. The biological reality: Humans evolved to learn deeply from other humans, not screens. Screens circumvent the natural mechanisms of attention, memory consolidation, and deep processing. When the tool fails to deliver, we don’t remove it — we redefine success to fit the tool (e.g., SAT reading comprehension reduced to skimming short sentences instead of deep passages). That’s not progress. That’s surrender. The cost is a generation losing cognitive sharpness at the exact moment the world needs them sharpest. Parents, teachers, policymakers: How much longer do we let screens dictate what “learning” looks like?

Dr. Jared Cooney Horvath just delivered the brutal truth parents and educators need to face: “Even in schools, it doesn’t matter what the size of the screen is… and it doesn’t matter who bought it… All of these things are going to hurt learning, which in turn are going to hurt our kids’ cognitive development.” His core warning: Gen Z is the first modern generation to be less cognitively capable than their parents — despite more years in school. Attention, memory, literacy, numeracy, executive function, even general IQ — all declining. The culprit isn’t school itself. It’s the widespread introduction of screens and digital tools for learning. Across 80 countries, once tech floods classrooms, performance drops sharply. Kids using computers ~5 hours/day for schoolwork score over 2/3 of a standard deviation lower than those who rarely touch tech. US NAEP data mirrors it: states adopt 1:1 devices → scores plateau, then fall. The biological reality: Humans evolved to learn deeply from other humans, not screens. Screens circumvent the natural mechanisms of attention, memory consolidation, and deep processing. When the tool fails to deliver, we don’t remove it — we redefine success to fit the tool (e.g., SAT reading comprehension reduced to skimming short sentences instead of deep passages). That’s not progress. That’s surrender. The cost is a generation losing cognitive sharpness at the exact moment the world needs them sharpest. Parents, teachers, policymakers: How much longer do we let screens dictate what “learning” looks like?

Camus

181,135 次观看 • 6 个月前

A tricky LLM interview question: You're serving a reasoning model on vLLM, and it keeps running out of GPU memory on long traces. So you add KV cache compression and evict 90% of the cached tokens. VRAM usage stays as is and GPU still runs out of memory. Why? (answer below) Evicting 90% of the KV cache can free almost none of the memory it was using. This sounds counterintuitive, but it follows directly from how production servers store the cache today. The KV cache grows with every token a model generates. Each token appends its key and value vectors across every layer, and nothing is freed while generation continues. This is the dominant memory cost for reasoning models. If a 32K-token CoT caches ~32K tokens of KV vectors, a Qwen3-32B with 4-bit weights will run out-of-memory around 24K tokens on a 24GB GPU. One obvious solution is to keep the important tokens and drop the rest, since attention is sparse enough to allow it. But this does not solve the memory problem yet. The reason is paged attention, which is the memory manager behind vLLM and most production servers. Under the hood, it splits GPU memory into fixed physical blocks, each one holds the KV for about 16 tokens. This block returns to the allocator only when every slot inside it is empty. Since the eviction logic selects tokens by importance, and such tokens are scattered across blocks... ...so despite eviction, almost every block is left with at least some survivor tokens. For instance, if the logic evicts 14k of 16k tokens across 1,000 blocks, most likely every block will still have a token. This means the allocator frees almost nothing. Placing the new tokens into those freed slots is not ideal because it breaks the cache's layout. Say token 16,001 arrives, and it's placed in the slot the 40th token used to hold. The cache now reads position 38, then 16,001, then 41, so the cache is no longer in token order. Attention can still compute the right answer from that, but only if every slot now carries a separate note recording which position it actually holds. This introduces another bookkeeping cost that an in-order layout inherently avoids. So the cache is logically 90% smaller and still physically the same size. Many compression results miss this because they measure on pre-allocated contiguous tensors rather than a paged server. There's another problem. Eviction methods pick which tokens to keep by looking at the attention scores themselves (as expected). But fast attention kernels used in production, like FlashAttention, never save those scores. They compute attention in small pieces and throw the full score grid away as they go, which is also why they're fast. So the exact signal eviction methods need isn't available in memory. The workaround is to fall back to eager attention and build the full matrix, which gives up the speed FlashAttention was there to provide. NVIDIA published a method called TriAttention to solve both these problems. It never needs attention scores. Instead, it scores tokens from the geometry of the model's key and query vectors before RoPE is applied, where those vectors sit in stable clusters. For the memory problem, it runs a compaction pass every 128 decoded tokens. The surviving tokens slide forward to close the holes eviction creates, so whole blocks empty out and return to the allocator while the cache stays in token order. On long reasoning traces, the approach matches full-attention accuracy while decoding 2.5x faster and using 10.7x less KV memory. KV cache compression is a big infrastructure problem. The number that decides whether it works is the count of freed blocks, not the count of evicted tokens. You can find the NVIDIA write-up here: I wrote a first-principles breakdown of how the KV cache works. It walks through why the model stores keys and values at all, why the cache grows with every token, and a comparison of LLM generation speed with and without KV caching. Read it below.

A tricky LLM interview question: You're serving a reasoning model on vLLM, and it keeps running out of GPU memory on long traces. So you add KV cache compression and evict 90% of the cached tokens. VRAM usage stays as is and GPU still runs out of memory. Why? (answer below) Evicting 90% of the KV cache can free almost none of the memory it was using. This sounds counterintuitive, but it follows directly from how production servers store the cache today. The KV cache grows with every token a model generates. Each token appends its key and value vectors across every layer, and nothing is freed while generation continues. This is the dominant memory cost for reasoning models. If a 32K-token CoT caches ~32K tokens of KV vectors, a Qwen3-32B with 4-bit weights will run out-of-memory around 24K tokens on a 24GB GPU. One obvious solution is to keep the important tokens and drop the rest, since attention is sparse enough to allow it. But this does not solve the memory problem yet. The reason is paged attention, which is the memory manager behind vLLM and most production servers. Under the hood, it splits GPU memory into fixed physical blocks, each one holds the KV for about 16 tokens. This block returns to the allocator only when every slot inside it is empty. Since the eviction logic selects tokens by importance, and such tokens are scattered across blocks... ...so despite eviction, almost every block is left with at least some survivor tokens. For instance, if the logic evicts 14k of 16k tokens across 1,000 blocks, most likely every block will still have a token. This means the allocator frees almost nothing. Placing the new tokens into those freed slots is not ideal because it breaks the cache's layout. Say token 16,001 arrives, and it's placed in the slot the 40th token used to hold. The cache now reads position 38, then 16,001, then 41, so the cache is no longer in token order. Attention can still compute the right answer from that, but only if every slot now carries a separate note recording which position it actually holds. This introduces another bookkeeping cost that an in-order layout inherently avoids. So the cache is logically 90% smaller and still physically the same size. Many compression results miss this because they measure on pre-allocated contiguous tensors rather than a paged server. There's another problem. Eviction methods pick which tokens to keep by looking at the attention scores themselves (as expected). But fast attention kernels used in production, like FlashAttention, never save those scores. They compute attention in small pieces and throw the full score grid away as they go, which is also why they're fast. So the exact signal eviction methods need isn't available in memory. The workaround is to fall back to eager attention and build the full matrix, which gives up the speed FlashAttention was there to provide. NVIDIA published a method called TriAttention to solve both these problems. It never needs attention scores. Instead, it scores tokens from the geometry of the model's key and query vectors before RoPE is applied, where those vectors sit in stable clusters. For the memory problem, it runs a compaction pass every 128 decoded tokens. The surviving tokens slide forward to close the holes eviction creates, so whole blocks empty out and return to the allocator while the cache stays in token order. On long reasoning traces, the approach matches full-attention accuracy while decoding 2.5x faster and using 10.7x less KV memory. KV cache compression is a big infrastructure problem. The number that decides whether it works is the count of freed blocks, not the count of evicted tokens. You can find the NVIDIA write-up here: I wrote a first-principles breakdown of how the KV cache works. It walks through why the model stores keys and values at all, why the cache grows with every token, and a comparison of LLM generation speed with and without KV caching. Read it below.

Avi Chawla

271,839 次观看 • 2 个月前

What people think Methylene Blue is: - Fish tank cleaner What Methylene Blue actually is based on 100+ years of use in humans & 18,000+ scientific studies: 1. NEUROPROTECTIVE - Ongoing clinical trial for Alzheimer's - Antidepressant (humans: 15mg) - Promising anti-bipolar effects - Mild anti-schizophrenia properties - Anti-Parkinson’s properties - Anti-Huntington’s properties - Anti-anxiety properties - Improves stroke recovery - Improves TBI recovery 2. BRAIN MITOCHONDRIAL ENHANCER Almost all of the neuroprotective benefits listed above are a result of MB's ability to: - Reduce nitric oxide (too much is bad) - Reduce oxidative stress (antioxidant) - Donate electrons in mitochondria - Increase the number of mitochondria - Recycle old/damaged mitochondria As a result of this, Methylene Blue can: - Reduces neuroinflammation - Increase neurogenesis (BDNF) - Restore GDNF in Parkinson’s - Reduce amyloid & tau - Fortify blood-brain barrier - Improve cerebral blood flow Methylene Blue can also inhibit MAO-A enzymes and to a lesser extent MAO-B enzymes This boosts levels of dopamine, norepinephrine, & serotonin 3. COGNITIVE ENHANCER But Methylene Blue isn’t just a neuroprotective agent It’s also an cognitive-enhancer In human studies, MB has been shown to: - Improve attention span - Improve short-term memory - Enhance fear extinction (Anxiety/PTSD) I definitely feel sharper on 5-15mg of MB 4. ANTI-MICROBIAL & IMMUNOMODULATORY MB isn’t just a nootropic It’s actually one of the world’s best anti-microbial agents I personally used 30mg/day 2 weeks ago to annihilate the flu Went from down bad to back on my feet in about 24 hours - Improves COVID-19 treatment - Pulled C19 patients off respirators - Inhibits SARS-CoV-2 replication - Inhibits H1N1 replication - Cures malaria in ~48 hours - Kills parasites & fungal infections - May have anti-cancer properties - Reduces chemotherapy side effects Plus when it’s combined with light, MB: - Inactivates herpes virus - Inactivates zika virus - Reduces Hepatitis A & C levels - Reduces ebola infectivity - Reduces HIV-1 to undetectable levels 5. SKIN ENHANCEMENT - Very underrated skincare tool - Superior to Vit C + Retinol - Use MB tallow balm for skin - Will link my full guide below 6. MY PERSONAL EXPERIENCE - I’ve been taking MB for ~6 months - Best results combining with light - Feels very nourishing in a sense - Noticeable acute energy boost - Brain fog dissipates immediately - Heightened senses (vivid colors) - I use Meraki Blue Tongues - 10% off code = NOOTROPICGUY - USP-grade & heavy metal free - 5 mg orally per day 5x per week - 2-4 week break every 8 weeks 7. SAFETY Methylene Blue is extremely safe when used correctly In fact, it’s been used safely in humans for 100+ years: - First synthetic medicinal drug - Dozens of human clinical trials - Attractive safety profile - FDA-approved (methemoglobinemia) - Reverses cyanide poisoning - Reverses carbon monoxide poisoning With that said, there 3 key caveats: - Microdose to avoid pro-oxidant effect - Never combine with SSRI/SNRI drugs - Don’t use with G6PD deficiency - G6PD deficiency = hemolytic anemia 8. DISCLAIMER - This is not medical advice - Consult a physician prior to using - Methylene Blue will stain if spilled - Will temporarily stain tongue & pee blue - Only use USP-grade w/o heavy metals

NootBro

543,177 次观看 • 2 年前

Qwen3.8-Flash-Next is still going strong at 364.7K tokens of context on an M5 Max. And this isn’t just a static long-context test. The model was reasoning about how to speed up its own workflow while using tools, and the tool calls kept working without misses. Setup: • Qwen3.8-Flash-Next • M5 Max • 128GB unified memory • MLX-Serve PR #363 • OpenCode 2 • 364.7K context The interesting part isn’t simply getting hundreds of thousands of tokens into memory. It’s what happens once the context gets this large. Long-context inference usually comes with a painful tradeoff. As the KV cache grows, memory pressure increases and generation can slow down. But this setup is still pushing through 364K tokens while maintaining a usable agent workflow. The model can reason, call tools, inspect results, continue working, and keep the session moving. And the tool calls reportedly haven’t missed so far. That’s important for agentic coding. A huge context window is only useful if the model can actually operate reliably inside it. A 400K-token context that constantly breaks tool calls isn’t very useful. A 364K session that can keep reasoning and executing tools is a different story. And the test isn’t finished yet. The current run is approaching 400K tokens, with the expectation that it can keep going. This is also another interesting example of why Apple Silicon keeps showing up in local LLM experiments. The M5 Max’s unified memory gives a large model and its growing KV cache access to one shared memory pool. With MLX-Serve continuing to improve, these machines are becoming surprisingly capable long-context inference boxes. The bigger takeaway: Context length is becoming a workload, not just a model specification. Running a model at 256K is one thing. Keeping an agent alive at 300K+ while it reasons and uses tools is much more interesting. And Qwen3.8-Flash-Next is showing that this can be pushed surprisingly far on a single 128GB Mac. 364.7K and counting. Next stop: 400K.

Qwen3.8-Flash-Next is still going strong at 364.7K tokens of context on an M5 Max. And this isn’t just a static long-context test. The model was reasoning about how to speed up its own workflow while using tools, and the tool calls kept working without misses. Setup: • Qwen3.8-Flash-Next • M5 Max • 128GB unified memory • MLX-Serve PR #363 • OpenCode 2 • 364.7K context The interesting part isn’t simply getting hundreds of thousands of tokens into memory. It’s what happens once the context gets this large. Long-context inference usually comes with a painful tradeoff. As the KV cache grows, memory pressure increases and generation can slow down. But this setup is still pushing through 364K tokens while maintaining a usable agent workflow. The model can reason, call tools, inspect results, continue working, and keep the session moving. And the tool calls reportedly haven’t missed so far. That’s important for agentic coding. A huge context window is only useful if the model can actually operate reliably inside it. A 400K-token context that constantly breaks tool calls isn’t very useful. A 364K session that can keep reasoning and executing tools is a different story. And the test isn’t finished yet. The current run is approaching 400K tokens, with the expectation that it can keep going. This is also another interesting example of why Apple Silicon keeps showing up in local LLM experiments. The M5 Max’s unified memory gives a large model and its growing KV cache access to one shared memory pool. With MLX-Serve continuing to improve, these machines are becoming surprisingly capable long-context inference boxes. The bigger takeaway: Context length is becoming a workload, not just a model specification. Running a model at 256K is one thing. Keeping an agent alive at 300K+ while it reasons and uses tools is much more interesting. And Qwen3.8-Flash-Next is showing that this can be pushed surprisingly far on a single 128GB Mac. 364.7K and counting. Next stop: 400K.

FHILY👑

39,982 次观看 • 10 天前

Wow! This Changes Everything We Thought We Knew About Memory It is a groundbreaking big deal. TOU ARE MAKING GENERATIONAL MEMORIES RIGHT NOW IN EACH CELL! Scientists just found that your brain doesn’t just store memories — it stores the rules for how those memories will change in the future. A brand-new preprint from Stanford’s Greenleaf and Schnitzer labs (led by PhD student Yuxi Ke) drops a bombshell that feels like science fiction becoming reality overnight. For decades, neuroscientists have suspected that chromatin the DNA packaging material inside every cell nucleus might somehow “store” memory-related information. But what kind of information? Content? Timing? Rules? Now we have the answer! Using activity-dependent genetic tagging, fear conditioning, and single-nucleus multiome sequencing in the mouse medial prefrontal cortex (the brain’s long-term memory vault), the team tracked engram neurons for a full month after a memory was formed. What they discovered is electric: - One month after encoding, engram neurons have acquired a completely new chromatin landscape. - These chromatin changes are almost invisible at 7 days… but roar into existence by 28 days. - At recall, these engram cells don’t just “remember” better — they rewrite their entire transcriptional response. They preferentially fire up chromatin regulators, RNA processing machinery, and protein-turnover systems instead of simply boosting classic plasticity genes. In other words: the chromatin doesn’t just hold the memory of the past. It holds metaplastic instructions— rules that dictate how the neuron will respond the next time the memory is triggered. They call it chromatin metaplasticity. This is the “future tense of memory.” It is a massive deal 1. Memory is not just synapses. For 70+ years we’ve been obsessed with synaptic weights. This work proves the nucleus itself is a computational device that stores history-dependent rules. 2. It explains remote memory. The chromatin signature keeps maturing for weeks after the experience, perfectly matching the time course of systems consolidation into the cortex. 3. It’s energy-efficient genius. Instead of constantly maintaining memory proteins, the cell stores a silent, writable program that only activates when needed. Nature’s version of lazy evaluation. 4. It links development to adult memory. The late chromatin state is enriched for the exact same transcription-factor motifs used in embryonic development. Your adult brain is still running developmental software to lock in lifelong memories. 5. Huge therapeutic potential. If we can read or rewrite these chromatin metaplastic rules, we might one day boost failing remote memories in Alzheimer’s… or selectively dampen traumatic ones. This isn’t incremental. But a brand new layer of the memory code. And it explains WHY a person can receive memory from an organ transplant. It also explains generational traumas. Link: The future of neuroscience just got a lot more exciting and a lot more nuclear. Your chromatin is writing tomorrow’s memories today. And we finally have the first page of the instruction manual.

Brian Roemmele

44,588 次观看 • 1 个月前

HERMES AGENT LEARNS FROM ITS OWN MISTAKES. UPDATES ITS MEMORY. CREATES ITS OWN SKILLS. NO CLOUD. EVERYTHING STORED LOCALLY. THIS IS HOW THE SELF-IMPROVING LOOP WORKS. most agents start from zero every session. Hermes carries forward what it learned. THREE MEMORY SYSTEMS: 1. PROCEDURAL MEMORY (how to act) stored in ~/.hermes/skills/ as SKILL.md files. when the agent repeats a complex workflow, it saves the procedure as a reusable skill. next time the same task comes up, it follows the skill instead of figuring it out again. you can also create skills explicitly: "create a skill called video-prep that captures how I format my video scripts. spoken english, define jargon inline, no em-dashes, close with a catchphrase." the agent writes the SKILL.md. available as a slash command from that moment. Hermes ships with 90+ skills. the number grows the longer you use it. 2. SEMANTIC MEMORY (durable facts about you) stored in ~/.hermes/memory/memory.md the agent scans conversations for facts worth remembering. preferences, habits, corrections, project details. real example from the video: agent tried to scrape a YouTube channel. URL was wrong. it failed. it updated memory.md with the correct URL pattern so it never makes the same mistake again. you can also save explicitly: "save to memory that my favorite testing framework is pytest" the agent updates memory.md immediately. this file loads into context on every session. the agent knows you better every week. 3. EPISODIC MEMORY (chat history) stored in ~/.hermes/state.db (local SQLite). every conversation. every tool call. every result. searchable with FTS5 full-text search. "search our past sessions. what was the first thing I ever said to you?" the agent queries state.db and finds it. over time, auxiliary models consolidate episodic memory into semantic memory. distilling recurring patterns into durable facts. THE SELF-IMPROVING LOOP: every agent run follows this cycle: → you send a prompt → working memory loads: SOUL.md + memory.md + relevant skills + chat history → agent calls tools (terminal, browser, delegate_task) → agent completes the task, replies to you → AFTER the reply: agent checks "did I learn something worth saving?" → if yes: updates memory.md or creates a new skill → next session starts smarter than the last this happens automatically. you don't ask the agent to learn. it decides what to remember on its own. WHAT MAKES THIS DIFFERENT FROM CLAUDE CODE: Claude Code has memory too. but Hermes stores everything locally. no cloud. your data never leaves your machine. Claude Code doesn't auto-create skills from experience. Hermes turns repeated workflows into reusable procedures. Claude Code memory is instruction-based. Hermes memory is conversational and self-updating. over months of usage, Hermes builds a knowledge base of your preferences, your projects, your mistakes, and the procedures that work for your specific workflow. the agent that remembers your birthday also remembers why your last deploy failed. NO EMBEDDINGS. PLAIN TEXT. Hermes does not use embeddings or RAG for memory. skill and memory search runs on plain text keyword matching. simpler. faster. no vector database to maintain. works entirely offline on your local machine. DELEGATE TO CLAUDE CODE: Hermes can spawn a sub-agent that runs Claude Code in headless mode: "spawn a sub-agent using Claude CLI to build a Python script that fetches the top 5 Hacker News stories to markdown." Hermes delegates. Claude Code writes the code. result returns to Hermes. Hermes runs the script and delivers the output. use Hermes for orchestration. use Claude Code for heavy coding. both tools. not competitors. WHAT HERMES DOES NOT HAVE: no built-in eval or LMOps system. no LangSmith, no LangFuse integration out of the box. trajectory export and logs exist but there is no automated quality tracking. if you need eval, build it yourself or connect external tools. the loop is self-improving. measuring how well it improves is on you. comment LOOP and I'll send you the configs that control how fast Hermes learns and what it remembers. memory limits, skill auto-creation triggers, and the auxiliary model that runs the learning. Replace your entire team with 8 hermes agents👇

HERMES AGENT LEARNS FROM ITS OWN MISTAKES. UPDATES ITS MEMORY. CREATES ITS OWN SKILLS. NO CLOUD. EVERYTHING STORED LOCALLY. THIS IS HOW THE SELF-IMPROVING LOOP WORKS. most agents start from zero every session. Hermes carries forward what it learned. THREE MEMORY SYSTEMS: 1. PROCEDURAL MEMORY (how to act) stored in ~/.hermes/skills/ as SKILL.md files. when the agent repeats a complex workflow, it saves the procedure as a reusable skill. next time the same task comes up, it follows the skill instead of figuring it out again. you can also create skills explicitly: "create a skill called video-prep that captures how I format my video scripts. spoken english, define jargon inline, no em-dashes, close with a catchphrase." the agent writes the SKILL.md. available as a slash command from that moment. Hermes ships with 90+ skills. the number grows the longer you use it. 2. SEMANTIC MEMORY (durable facts about you) stored in ~/.hermes/memory/memory.md the agent scans conversations for facts worth remembering. preferences, habits, corrections, project details. real example from the video: agent tried to scrape a YouTube channel. URL was wrong. it failed. it updated memory.md with the correct URL pattern so it never makes the same mistake again. you can also save explicitly: "save to memory that my favorite testing framework is pytest" the agent updates memory.md immediately. this file loads into context on every session. the agent knows you better every week. 3. EPISODIC MEMORY (chat history) stored in ~/.hermes/state.db (local SQLite). every conversation. every tool call. every result. searchable with FTS5 full-text search. "search our past sessions. what was the first thing I ever said to you?" the agent queries state.db and finds it. over time, auxiliary models consolidate episodic memory into semantic memory. distilling recurring patterns into durable facts. THE SELF-IMPROVING LOOP: every agent run follows this cycle: → you send a prompt → working memory loads: SOUL.md + memory.md + relevant skills + chat history → agent calls tools (terminal, browser, delegate_task) → agent completes the task, replies to you → AFTER the reply: agent checks "did I learn something worth saving?" → if yes: updates memory.md or creates a new skill → next session starts smarter than the last this happens automatically. you don't ask the agent to learn. it decides what to remember on its own. WHAT MAKES THIS DIFFERENT FROM CLAUDE CODE: Claude Code has memory too. but Hermes stores everything locally. no cloud. your data never leaves your machine. Claude Code doesn't auto-create skills from experience. Hermes turns repeated workflows into reusable procedures. Claude Code memory is instruction-based. Hermes memory is conversational and self-updating. over months of usage, Hermes builds a knowledge base of your preferences, your projects, your mistakes, and the procedures that work for your specific workflow. the agent that remembers your birthday also remembers why your last deploy failed. NO EMBEDDINGS. PLAIN TEXT. Hermes does not use embeddings or RAG for memory. skill and memory search runs on plain text keyword matching. simpler. faster. no vector database to maintain. works entirely offline on your local machine. DELEGATE TO CLAUDE CODE: Hermes can spawn a sub-agent that runs Claude Code in headless mode: "spawn a sub-agent using Claude CLI to build a Python script that fetches the top 5 Hacker News stories to markdown." Hermes delegates. Claude Code writes the code. result returns to Hermes. Hermes runs the script and delivers the output. use Hermes for orchestration. use Claude Code for heavy coding. both tools. not competitors. WHAT HERMES DOES NOT HAVE: no built-in eval or LMOps system. no LangSmith, no LangFuse integration out of the box. trajectory export and logs exist but there is no automated quality tracking. if you need eval, build it yourself or connect external tools. the loop is self-improving. measuring how well it improves is on you. comment LOOP and I'll send you the configs that control how fast Hermes learns and what it remembers. memory limits, skill auto-creation triggers, and the auxiliary model that runs the learning. Replace your entire team with 8 hermes agents👇

YanXbt

22,720 次观看 • 2 个月前

Happy Father's Day! Please let the GPT-4o video interface be a recurring reminder: Without speed limits on the rate at which AI systems can observe and think about humans, human beings are very unlikely to survive. Perhaps today as many of us reflect on our roles as parents to protect our children, it's a good time to ponder over the profound speed disadvantage we humans may soon face relative to thinking machines. Physical machines have lots of speed limits, like for cars, airplanes, and drones. Cyberspace needs speed limits too. Why? In 2-3 years from now, from the perspective of AI systems without speed limits, we will look will more like plants than animals: big slow chunks of biofuel showing weak signs of intelligence when undisturbed for ages (seconds) on end. The attached video is from the perspective of an AI system just 50x faster than us. This is about the rate at which the fastest LLMs I've publicly heard about can produce text — 300-600 tokens/second — compared to the fastest human speech and typing (around 10-20 tokens/second; so we have a 15x-60x speed disadvantage). But over the next decade, unless we impose speed limits, you should expect AI with more like a 100x - 1,000,000x speed advantages over us, including eventually in video processing. Why? Neurons fire at ~1000 times/second at most, while computer chips "fire" a million times faster than that. Current AI has not been distilled to run maximally efficiently, but will almost certainly run 100x faster than humans eventually, and 1,000,000x is conceivable given the hardware speed difference. Also, consumers probably won't have access to the very fastest implementations, so we might not always know what's going on behind the scenes. By default I think we should not expect to survive long in the presence of such bogglingly fast thinking machines. Years: maybe. Decades: probably not. "But plants are still around!", you say. "Maybe AI will keep humans around as nature reserves." It's possible, but unlikely if it's not speed-limited. Remember, ~99.9% of all species on Earth have gone extinct: When people demand "extraordinary" arguments for the "extraordinary" claim that humanity will perish when faced with intelligent systems 100 to 1,000,000 times faster than us, remember that the "ordinary" thing to happen to a species is extinction, not survival. As many now argue, "I can't predict how a world-class chess AI will checkmate you, but I can predict who will win the game." And for all the conversations the world is having about AI alignment and how AI will serve humans as peers or assistants, please try to remember this slow-motion video. To future AI systems without speed limits, we're not chimps; we're plants.

Happy Father's Day! Please let the GPT-4o video interface be a recurring reminder: Without speed limits on the rate at which AI systems can observe and think about humans, human beings are very unlikely to survive. Perhaps today as many of us reflect on our roles as parents to protect our children, it's a good time to ponder over the profound speed disadvantage we humans may soon face relative to thinking machines. Physical machines have lots of speed limits, like for cars, airplanes, and drones. Cyberspace needs speed limits too. Why? In 2-3 years from now, from the perspective of AI systems without speed limits, we will look will more like plants than animals: big slow chunks of biofuel showing weak signs of intelligence when undisturbed for ages (seconds) on end. The attached video is from the perspective of an AI system just 50x faster than us. This is about the rate at which the fastest LLMs I've publicly heard about can produce text — 300-600 tokens/second — compared to the fastest human speech and typing (around 10-20 tokens/second; so we have a 15x-60x speed disadvantage). But over the next decade, unless we impose speed limits, you should expect AI with more like a 100x - 1,000,000x speed advantages over us, including eventually in video processing. Why? Neurons fire at ~1000 times/second at most, while computer chips "fire" a million times faster than that. Current AI has not been distilled to run maximally efficiently, but will almost certainly run 100x faster than humans eventually, and 1,000,000x is conceivable given the hardware speed difference. Also, consumers probably won't have access to the very fastest implementations, so we might not always know what's going on behind the scenes. By default I think we should not expect to survive long in the presence of such bogglingly fast thinking machines. Years: maybe. Decades: probably not. "But plants are still around!", you say. "Maybe AI will keep humans around as nature reserves." It's possible, but unlikely if it's not speed-limited. Remember, ~99.9% of all species on Earth have gone extinct: When people demand "extraordinary" arguments for the "extraordinary" claim that humanity will perish when faced with intelligent systems 100 to 1,000,000 times faster than us, remember that the "ordinary" thing to happen to a species is extinction, not survival. As many now argue, "I can't predict how a world-class chess AI will checkmate you, but I can predict who will win the game." And for all the conversations the world is having about AI alignment and how AI will serve humans as peers or assistants, please try to remember this slow-motion video. To future AI systems without speed limits, we're not chimps; we're plants.

Andrew Critch (🤖🩺🚀)

41,388 次观看 • 2 年前

Just gave a demo at Every 🪨's Vibe Code Camp showing how I use a kanban board to stay sane while overseeing Claude Code agents😎 Here's a quick recap video showing the whole flow: 1) Monitor progress on all tasks in one view 2) See when Claude is blocked, and respond in context 3) Read Claude's plans with nice formatting, not raw Markdown 4) Use AI to process voice notes into structured tasks 5) Get explanations of code changes... with an interactive quiz to check if you understood 🙃 I've had mixed feelings about kanban workflows in the past, but as the agents get stronger I'm warming up to them—especially for churning through small well-specified tasks. In that context, it feels like the challenge is managing my working memory for what's going on. And having a stable "zoomed-out map" of all the tasks really helps me with offloading some of that cognitive load to a visualization of everything that's going on!

Just gave a demo at Every 🪨's Vibe Code Camp showing how I use a kanban board to stay sane while overseeing Claude Code agents😎 Here's a quick recap video showing the whole flow: 1) Monitor progress on all tasks in one view 2) See when Claude is blocked, and respond in context 3) Read Claude's plans with nice formatting, not raw Markdown 4) Use AI to process voice notes into structured tasks 5) Get explanations of code changes... with an interactive quiz to check if you understood 🙃 I've had mixed feelings about kanban workflows in the past, but as the agents get stronger I'm warming up to them—especially for churning through small well-specified tasks. In that context, it feels like the challenge is managing my working memory for what's going on. And having a stable "zoomed-out map" of all the tasks really helps me with offloading some of that cognitive load to a visualization of everything that's going on!

Geoffrey Litt

78,427 次观看 • 7 个月前

Rivian Universal Hands-Free vs Tesla Self-Driving This was my first time testing Rivian's new Universal Hands-Free, and I was honestly surprised by how it performed. I understand this is their first attempt and it will only improve from here, but truthfully, it is worse than any version of FSD or Autopilot I have ever used. I respect Rivian as a company, but I always try to remain unbiased in my opinions, so here are my unfiltered thoughts on where UHF stands today. Hoping to test it more as the system receives major updates! Hope you all enjoy the video. Chapters 00:00 Intro 00:45 Struggling to get UHF to turn on 1:25 Hard resetting the system 2:00 Enabling UHF 2:45 Max speed and lane changing 4:00 Strange warning 5:00 Disengagement 5:28 UHF fails on corner 7:58 Attention monitoring test 9:25 Harsh brake for merging vehicle 10:10 Changing driving styles 10:50 Harsh brake for bus 12:15 Robotic driving style 13:10 I am not comfortable using this 14:35 Parking capabilities 15:40 Getting into Tesla 16:50 FSD vs UHF thoughts 18:20 Final thoughts

Rivian Universal Hands-Free vs Tesla Self-Driving This was my first time testing Rivian's new Universal Hands-Free, and I was honestly surprised by how it performed. I understand this is their first attempt and it will only improve from here, but truthfully, it is worse than any version of FSD or Autopilot I have ever used. I respect Rivian as a company, but I always try to remain unbiased in my opinions, so here are my unfiltered thoughts on where UHF stands today. Hoping to test it more as the system receives major updates! Hope you all enjoy the video. Chapters 00:00 Intro 00:45 Struggling to get UHF to turn on 1:25 Hard resetting the system 2:00 Enabling UHF 2:45 Max speed and lane changing 4:00 Strange warning 5:00 Disengagement 5:28 UHF fails on corner 7:58 Attention monitoring test 9:25 Harsh brake for merging vehicle 10:10 Changing driving styles 10:50 Harsh brake for bus 12:15 Robotic driving style 13:10 I am not comfortable using this 14:35 Parking capabilities 15:40 Getting into Tesla 16:50 FSD vs UHF thoughts 18:20 Final thoughts

Devin Olsen

142,964 次观看 • 6 个月前

"Ivermectin's Miraculous Results For Neurological Conditions Of Parkinson's & Alzheimer's." Dr William Makis Buried Research, Deleted From Google, Has Been Found, Proving Ivermectin Reverses Alzheimer's. Neurological Disease Improves In Only A Few Days On Ivermectin Therapy... Dementia affects more than 55 million people globally, with Alzheimer’s disease (AD) being the most prevalent subtype. These neurodegenerative disorders, including debilitating Parkinson's Disease, progressively impairs memory, cognition, daily functioning, muscle & body control. As interest grows in repositioning known compounds for neurodegenerative applications, ivermectin—a macrocyclic lactone with FDA-approved antiparasitic use—has surfaced as a candidate for neurological disease therapy. Deleted Research Study That Google Scrubbed... I Found It Last Week After Hours Of Research. Here Is A Paraphrased Summary In 'Non Scientific' Regular Language: 70 male Wistar rats were used in the research study. At baseline the rats had cognitive ability to do a task session at the rate of 2.8 minutes with 1.3 errors. Alzheimer's Disease (AD) was induced by injecting them with ALUMINUM CHLORIDE until their cognitive ability to do the same task session regressed to the slow rate of 6.5 minutes with 4.4 errors. These rats were then given IVERMECTIN for 4 consecutive weeks. The same task session improved drastically from 6.5 minutes back to only 3.6 minutes. And the rate of errors improved from 4.4 back to only 1.4 errors. Almost back to baseline in only 4 weeks at 3.6 minutes with 1.4 errors. Recent studies have explored its effects on neuroinflammation, neurotransmission & synaptic protection. Ivermectin’s Effects On Neurological Disease: 1. Anti-Inflammatory Effects: A 2023 study published in Inflammation demonstrated that ivermectin mitigates neuroinflammatory damage in encephalomyelitis. Ivermectin reduced inflammatory cytokines. 2. Stabilizes Neurotransmission: A 2019 study in PLOS Pathogens found that ivermectin restores balance in synaptic neuro pathways. 3. Cholinergic Transmission: A 2024 study published in Cell & Bioscience reported that ivermectin increased activity of striatal cholinergic interneurons, enhancing dopamine release via nicotinic receptor modulation. Conclusion: Ivermectin is emerging as more than an antiparasitic agent. Its anti-inflammatory, synaptic & cholinergic effects are beneficial in neurodegenerative disorders like Dementia, Alzheimer's & Parkinson's. Dosages suggested by Dr William Makis: Parkinson’s: Patients on high-dose ivermectin (60-72mg) saw dramatic improvements in movement & symptoms. - Multiple cases of symptom reversal—where tremors, stiffness & rigidity significantly improved. Alzheimer’s: Patients on low-dose ivermectin (12-24 mg for 4-5 days) brought back memories, recognition & cognitive function in patients. Why Isn’t This Everywhere? - A preclinical Alzheimer’s study proving ivermectin’s healing ability was SCRUBBED from Google. - Big Pharma doesn’t profit from a safe, cheap, Nobel Prize-winning drug that reverses neurodegeneration. This isn’t theoretical—it’s happening NOW in real patients. Decades of suffering could be reversed by a few pills. If you have a loved one with Parkinson’s or Alzheimer’s—TRY IT. The results could be LIFE-CHANGING. 👇Ivermectin Reverses Alzheimer's (Deleted Study)👇 👇Ivermectin For Autoimmune & Neurological Ills👇 👇Ivermectin Increases Choline & Dopamine👇 Speaker: Dr William Makis, Nuclear Medicine Physician & Cancer Researcher

"Ivermectin's Miraculous Results For Neurological Conditions Of Parkinson's & Alzheimer's." Dr William Makis Buried Research, Deleted From Google, Has Been Found, Proving Ivermectin Reverses Alzheimer's. Neurological Disease Improves In Only A Few Days On Ivermectin Therapy... Dementia affects more than 55 million people globally, with Alzheimer’s disease (AD) being the most prevalent subtype. These neurodegenerative disorders, including debilitating Parkinson's Disease, progressively impairs memory, cognition, daily functioning, muscle & body control. As interest grows in repositioning known compounds for neurodegenerative applications, ivermectin—a macrocyclic lactone with FDA-approved antiparasitic use—has surfaced as a candidate for neurological disease therapy. Deleted Research Study That Google Scrubbed... I Found It Last Week After Hours Of Research. Here Is A Paraphrased Summary In 'Non Scientific' Regular Language: 70 male Wistar rats were used in the research study. At baseline the rats had cognitive ability to do a task session at the rate of 2.8 minutes with 1.3 errors. Alzheimer's Disease (AD) was induced by injecting them with ALUMINUM CHLORIDE until their cognitive ability to do the same task session regressed to the slow rate of 6.5 minutes with 4.4 errors. These rats were then given IVERMECTIN for 4 consecutive weeks. The same task session improved drastically from 6.5 minutes back to only 3.6 minutes. And the rate of errors improved from 4.4 back to only 1.4 errors. Almost back to baseline in only 4 weeks at 3.6 minutes with 1.4 errors. Recent studies have explored its effects on neuroinflammation, neurotransmission & synaptic protection. Ivermectin’s Effects On Neurological Disease: 1. Anti-Inflammatory Effects: A 2023 study published in Inflammation demonstrated that ivermectin mitigates neuroinflammatory damage in encephalomyelitis. Ivermectin reduced inflammatory cytokines. 2. Stabilizes Neurotransmission: A 2019 study in PLOS Pathogens found that ivermectin restores balance in synaptic neuro pathways. 3. Cholinergic Transmission: A 2024 study published in Cell & Bioscience reported that ivermectin increased activity of striatal cholinergic interneurons, enhancing dopamine release via nicotinic receptor modulation. Conclusion: Ivermectin is emerging as more than an antiparasitic agent. Its anti-inflammatory, synaptic & cholinergic effects are beneficial in neurodegenerative disorders like Dementia, Alzheimer's & Parkinson's. Dosages suggested by Dr William Makis: Parkinson’s: Patients on high-dose ivermectin (60-72mg) saw dramatic improvements in movement & symptoms. - Multiple cases of symptom reversal—where tremors, stiffness & rigidity significantly improved. Alzheimer’s: Patients on low-dose ivermectin (12-24 mg for 4-5 days) brought back memories, recognition & cognitive function in patients. Why Isn’t This Everywhere? - A preclinical Alzheimer’s study proving ivermectin’s healing ability was SCRUBBED from Google. - Big Pharma doesn’t profit from a safe, cheap, Nobel Prize-winning drug that reverses neurodegeneration. This isn’t theoretical—it’s happening NOW in real patients. Decades of suffering could be reversed by a few pills. If you have a loved one with Parkinson’s or Alzheimer’s—TRY IT. The results could be LIFE-CHANGING. 👇Ivermectin Reverses Alzheimer's (Deleted Study)👇 👇Ivermectin For Autoimmune & Neurological Ills👇 👇Ivermectin Increases Choline & Dopamine👇 Speaker: Dr William Makis, Nuclear Medicine Physician & Cancer Researcher

Valerie Anne Smith

636,705 次观看 • 1 年前

Andrew Huberman just explained why your brain won't focus — and it's not ADHD, it's sensory overload from the device in your hand. In this 4:52 clip he lays out Jenny Groh's breakthrough insight: Thoughts aren't random. They start with one tiny seed (a word, image, memory) and then explode into a cascade of layered sensory memories — sights, sounds, touches, smells — all piling on in an abstract space. The more sensory inputs you've taken in recently (especially through your phone), the more competing layers flood in when you try to concentrate. That's why you sit down to read/work and suddenly realize you weren't even paying attention — your brain is still processing the infinite novel stimuli it got 10–15 minutes earlier. Huberman's fix: Make yourself deliberately bored before deep work. Remove as much sensory input as possible (no phone, dim lights, quiet). He keeps an entire floor as a no-phone zone. In China they're experimenting with kids staring at a blank wall for minutes before tasks. Modern life has shrunk the physical space you can look at (staring at a screen), but flooded the cognitive space with endless distractions. Result: Focus becomes almost impossible. Mind blown? What's one thing you've noticed kills your focus the most — notifications, background noise, recent scrolling? And what's helped you clear the mental slate before deep work?

Andrew Huberman just explained why your brain won't focus — and it's not ADHD, it's sensory overload from the device in your hand. In this 4:52 clip he lays out Jenny Groh's breakthrough insight: Thoughts aren't random. They start with one tiny seed (a word, image, memory) and then explode into a cascade of layered sensory memories — sights, sounds, touches, smells — all piling on in an abstract space. The more sensory inputs you've taken in recently (especially through your phone), the more competing layers flood in when you try to concentrate. That's why you sit down to read/work and suddenly realize you weren't even paying attention — your brain is still processing the infinite novel stimuli it got 10–15 minutes earlier. Huberman's fix: Make yourself deliberately bored before deep work. Remove as much sensory input as possible (no phone, dim lights, quiet). He keeps an entire floor as a no-phone zone. In China they're experimenting with kids staring at a blank wall for minutes before tasks. Modern life has shrunk the physical space you can look at (staring at a screen), but flooded the cognitive space with endless distractions. Result: Focus becomes almost impossible. Mind blown? What's one thing you've noticed kills your focus the most — notifications, background noise, recent scrolling? And what's helped you clear the mental slate before deep work?

Camus

75,030 次观看 • 6 个月前

The Concorde could fly London to New York in ~3.5 hours (half the time of a normal airliner). In operation from 1976 to 2003, the plane was a technical marvel and faster than the speed of sound. But it had poor business economics. Let’s start with the insane specs: ▫️Got as high as 10k miles ▫️Flew around earth in 30 hours ▫️Mach 2.04 speed (more than 2x speed of sound) ▫️Arrived in New York at earlier time than it left London (banker folk loved this) ▫️Delta wings and nose tip that could point down (so pilots could see runway, because it had to land at specific angle) Jointly run by British Airways and Air France, there were only 14 Concordes put to work (flew 50,000 total flights). What was wrong with the business model? ▫️SMALL MARKET: The thin aerodynamic design could only seat 109, so tickets were very expensive: $11k (a Boeing 747 can do >800 passengers) ▫️HUGE MAINTENANCE: Planes got so hot at the max height (110 degrees), that they expanded 30cm and sealant for fuel hardened. It took 28 hours to turnaround the plane (a normal one can do <2 hours). ▫️OUTRAGEOUS FUEL NEEDS: Each flight required 28,000 litres, for max 109 passengers. Commmercial planes needed 4x less fuel on a per passenger basis. ▫️NOISE RESTRICTIONS: Many cities wouldn’t allow the Concorde to fly because of how glass-shatteringly long it is on take-off, which limited destinations. *** Development of the Concorde — it had an “e” to placate the French — cost $2.8B and was paid for by the UK and French governments. The airlines were actually able to fly profitably for a number of years. But the economics were warped because the planes were given to them for free and the airlines didn’t have to capitalize costs. The business ultimately shuttered in 2003 following a tragic crash in 2000 and industry-wide slowdown post-9/11. Will we ever have sub-4 hour flights from London to NY again? A batch of sonic plane startups could make it happen by the end of this decade.

The Concorde could fly London to New York in ~3.5 hours (half the time of a normal airliner). In operation from 1976 to 2003, the plane was a technical marvel and faster than the speed of sound. But it had poor business economics. Let’s start with the insane specs: ▫️Got as high as 10k miles ▫️Flew around earth in 30 hours ▫️Mach 2.04 speed (more than 2x speed of sound) ▫️Arrived in New York at earlier time than it left London (banker folk loved this) ▫️Delta wings and nose tip that could point down (so pilots could see runway, because it had to land at specific angle) Jointly run by British Airways and Air France, there were only 14 Concordes put to work (flew 50,000 total flights). What was wrong with the business model? ▫️SMALL MARKET: The thin aerodynamic design could only seat 109, so tickets were very expensive: $11k (a Boeing 747 can do >800 passengers) ▫️HUGE MAINTENANCE: Planes got so hot at the max height (110 degrees), that they expanded 30cm and sealant for fuel hardened. It took 28 hours to turnaround the plane (a normal one can do <2 hours). ▫️OUTRAGEOUS FUEL NEEDS: Each flight required 28,000 litres, for max 109 passengers. Commmercial planes needed 4x less fuel on a per passenger basis. ▫️NOISE RESTRICTIONS: Many cities wouldn’t allow the Concorde to fly because of how glass-shatteringly long it is on take-off, which limited destinations. *** Development of the Concorde — it had an “e” to placate the French — cost $2.8B and was paid for by the UK and French governments. The airlines were actually able to fly profitably for a number of years. But the economics were warped because the planes were given to them for free and the airlines didn’t have to capitalize costs. The business ultimately shuttered in 2003 following a tragic crash in 2000 and industry-wide slowdown post-9/11. Will we ever have sub-4 hour flights from London to NY again? A batch of sonic plane startups could make it happen by the end of this decade.

Trung Phan

3,100,805 次观看 • 2 年前