Olivia
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CryptoAkaza 🌬️🌸❄️🌙
@CryptoAkaza00
crypto since 2017 | backing builders & co-founding meaningful things | a scientific luck generator & lifelong student of the game | anon backup account
Hong Kong
Joined October 2021
836 Following
9 Followers
212 Posts
The curse of discipline is that every day looks the same. The curse of indiscipline is that every year does.
JUST IN🚨: NASA Physicist Thomas Campbell states the universe tracks every human decision to measure individual spiritual growth.
This system operates like a cosmic feedback loop, testing whether our avatars choose love over chaos.
https://t.co/PTeDiv1b6L
凡是需要别人点头才算数的成功，都是社会游戏。凡是只看现实结果、不看谁认可的成功，才属于现实世界。
桥会不会塌，药有没有效，飞机能不能飞，产品有没有人买，这些事不在乎你的是谁，只认结果。
学位，职称，工作头衔，各种奖、排行榜、蓝V、粉丝数，点击量，移民身份，行业知名度，这些得别人说你行，你才行。
自然界问：这是真的吗。
市场问：有人愿意付钱吗。
社会问：大家认可吗。
大多数人一辈子困在第三个问题里。
Announcing a new division of Midjourney called "Midjourney Medical"
@Mubarak_mubious “How to Speak” by Patrick Winston, a brilliant computer scientist and the director of the MIT Artificial Intelligence Laboratory for 25 years. He became legendary for his ANNUAL Independent Activities Period (IAP) lecture titled "How to Speak." Tier-1, timeless & super helpful!
This is cool. A Nature feature about my project.
Many scientists dismiss n=1 experimentation. Some actively work to diminish it.
I disagree. N=1 has a role, and an increasingly important one.
My team and I use clinical trial evidence daily. It guides our decision making and it also has limits.
Most people are struggling with a health issue that ranges anywhere from annoying to life threatening. Clinical evidence doesn't always provide what's needed to act. Personal experimentation and measurement can help fill that gap.
AI, multi-omics, wearables and real-time tracking all empower the N=1 to tackle conditions that remain incurable by current medical standards.
Here is the complete write up we provided to the author:
Randomized trials are necessary but not sufficient: the case for N-of-1 measurement and experimentation
Bryan Johnson and the Blueprint & Immortals Medical and Science Team: Ali Ghanem, PhD, Damon Forbes, M.D., and Carl Seger, M.D.
Randomized controlled trials remain the gold standard for evaluating therapeutic claims, and they will continue to anchor the evidence we use to select interventions. We do not dispute this. Our argument is narrower.
The RCT is engineered to estimate the average treatment effect of a single intervention, under a fixed protocol, in a defined population.
That is exactly the right instrument for the question it was built to answer: whether an intervention works on average, and whether it is safe enough to use. It is the wrong instrument for the question that governs health and lifespan optimization: which combination of interventions, at which doses, produces the best response in a specific individual measured densely over time.
By design, the RCT averages over the very heterogeneity that personalization must exploit; its narrow endpoints, short horizons, and one-intervention-at-a-time structure are not built to resolve individual response. This limitation is structural, not a defect to be corrected with larger samples.
N=1 experiments with longitudinal, high-resolution measurement are therefore not a substitute for RCTs but a complement: the means by which population-level evidence is translated into the individually optimized, proactively preventive interventions that meaningful health and lifespan extension require.
Trials report binary endpoints and population averages that do not capture individual response [1]. In JUPITER, the number needed to treat to prevent one major cardiovascular event over five years was 252; by that endpoint, roughly 96% of treated participants gained no measurable benefit. It may be objected that the trial did measure continuous biology, since median LDL cholesterol fell by about 50% [2]. But such a biomarker change does not, by itself, secure approval or guide therapy; the decision rests on the binary endpoint.
The continuous signal is recorded yet not acted upon, other continuous changes go untracked, and none of these markers is measured in the individual patient in routine care. Neither clinician nor patient can therefore identify who benefits, who could safely forgo treatment, or who is silently accruing harm. Safety inherits the same blind spot: trials register only what they measure, or events too severe to miss.
One instructive example is rofecoxib. Approved in 1999 on analgesic and gastrointestinal endpoints, it carried none for cardiovascular harm; the signal in VIGOR (2000) [3] was read as naproxen’s protection until APPROVe settled it in 2004 [4].
But these were endpoint-based trials, built to count events, not to measure the prostacyclin-thromboxane shift that would have made the prothrombotic risk visible before it appeared as infarctions. It was not so much missed as never measured: a structural shortcoming of endpoint-based design. Negative results, moreover, are disproportionately unpublished [5], so the evidence base loses the findings that would most constrain prevailing hypotheses.
We regard N-of-1 measurement as the next frontier, enabled by the convergence of artificial intelligence, wearables, multi-omic profiling, real-time tracking, and exposome capture.
Within my own protocols, maximally quantified and self-controlled, we have already generated signals that lie beyond the published literature and constitute first-in-human observations: real-time quantification of heat-shock-protein activation against continuously measured core temperature; sauna-driven clearance of environmental organic toxins (including plasticizers like phthalates and DEHP); enhanced fertility markers under sauna with testicular cooling; a first proof-of-principle demonstration of microplastics complete elimination from the semen and by more than 90% in blood, against a background in which these particles have been found in the semen [6] and testes [7] of every man so far studied; and the first human signal of a psilocybin-induced metabolic switch that lowered blood glucose and durably improved glycemic control.
We treat all of these as observations in need of further validation; our framework is not about finding instant answers but about identifying which questions are worth asking and investigating further.
The paradigm scales. A few deeply instrumented pioneers establish the axes of a high-dimensional physiological space; a larger cohort of cartographers charts its breadth across longitudinal individuals.
Once charted, the measurement burden collapses: neighbour-embedding methods8 position a sparsely sampled person within the map of the high-dimensional individual-biomarker space, inferring state and trajectory from nearest neighbours. This is already routine in biology, where sparse genotyping arrays are imputed to whole-genome resolution against reference panels [9], and, beyond medicine, where roughly 300 social-media “likes” predicted personality better than a spouse [10]. The deep cost is paid once; thereafter each individual is situated cheaply.
The frontier we find most compelling is the unleashing of N-of-1 methods against conditions presently considered incurable. Individualized, biomarker-guided strategies are already converting such conditions from managed to treatable: a documented case of recurrent osteosarcoma brought to sustained remission under an intensive personalized regimen, and, in veterinary oncology, the first personalized mRNA cancer vaccine administered to a dog, which produced substantial tumour regression. The new focus of our protocol is to tackle chronic conditions that current medicine accepts as manageable but not treatable, and to render them treatable through advanced diagnostics and next-generation personalized therapeutics.
References
1. Kent, D. M., Steyerberg, E. & van Klaveren, D. Personalized evidence based medicine: predictive approaches to heterogeneous treatment effects. BMJ 363, k4245 (2018).
2. Ridker, P. M. et al. Rosuvastatin to prevent vascular events in men and women with elevated C-reactive protein. N. Engl. J. Med. 359, 2195–2207 (2008).
3. Bombardier, C. et al. Comparison of upper gastrointestinal toxicity of rofecoxib and naproxen in patients with rheumatoid arthritis (VIGOR Study Group). N. Engl. J. Med. 343, 1520–1528 (2000).
4. Bresalier, R. S. et al. Cardiovascular events associated with rofecoxib in a colorectal adenoma chemoprevention trial (APPROVe). N. Engl. J. Med. 352, 1092–1102 (2005).
5. Turner, E. H. et al. Selective publication of antidepressant trials and its influence on apparent efficacy. N. Engl. J. Med. 358, 252–260 (2008).
6. Li, N. et al. Prevalence and implications of microplastic contaminants in general human seminal fluid: a Raman spectroscopic study. Sci. Total Environ. 937, 173522 (2024).
7. Hu, C. J. et al. Microplastic presence in dog and human testis and its potential association with sperm count and weights of testis and epididymis. Toxicol. Sci. 200, 235–240 (2024).
8. van der Maaten, L. & Hinton, G. Visualizing data using t-SNE. J. Mach. Learn. Res. 9, 2579–2605 (2008).
9. Marchini, J. & Howie, B. Genotype imputation for genome-wide association studies. Nat. Rev. Genet. 11, 499–511 (2010).
10. Youyou, W., Kosinski, M. & Stillwell, D. Computer-based personality judgments are more accurate than those made by humans. Proc. Natl. Acad. Sci. USA 112, 1036–1040 (2015).
![bryan_johnson's tweet photo. This is cool. A Nature feature about my project.
Many scientists dismiss n=1 experimentation. Some actively work to diminish it.
I disagree. N=1 has a role, and an increasingly important one.
My team and I use clinical trial evidence daily. It guides our decision making and it also has limits.
Most people are struggling with a health issue that ranges anywhere from annoying to life threatening. Clinical evidence doesn't always provide what's needed to act. Personal experimentation and measurement can help fill that gap.
AI, multi-omics, wearables and real-time tracking all empower the N=1 to tackle conditions that remain incurable by current medical standards.
Here is the complete write up we provided to the author:
Randomized trials are necessary but not sufficient: the case for N-of-1 measurement and experimentation
Bryan Johnson and the Blueprint & Immortals Medical and Science Team: Ali Ghanem, PhD, Damon Forbes, M.D., and Carl Seger, M.D.
Randomized controlled trials remain the gold standard for evaluating therapeutic claims, and they will continue to anchor the evidence we use to select interventions. We do not dispute this. Our argument is narrower.
The RCT is engineered to estimate the average treatment effect of a single intervention, under a fixed protocol, in a defined population.
That is exactly the right instrument for the question it was built to answer: whether an intervention works on average, and whether it is safe enough to use. It is the wrong instrument for the question that governs health and lifespan optimization: which combination of interventions, at which doses, produces the best response in a specific individual measured densely over time.
By design, the RCT averages over the very heterogeneity that personalization must exploit; its narrow endpoints, short horizons, and one-intervention-at-a-time structure are not built to resolve individual response. This limitation is structural, not a defect to be corrected with larger samples.
N=1 experiments with longitudinal, high-resolution measurement are therefore not a substitute for RCTs but a complement: the means by which population-level evidence is translated into the individually optimized, proactively preventive interventions that meaningful health and lifespan extension require.
Trials report binary endpoints and population averages that do not capture individual response [1]. In JUPITER, the number needed to treat to prevent one major cardiovascular event over five years was 252; by that endpoint, roughly 96% of treated participants gained no measurable benefit. It may be objected that the trial did measure continuous biology, since median LDL cholesterol fell by about 50% [2]. But such a biomarker change does not, by itself, secure approval or guide therapy; the decision rests on the binary endpoint.
The continuous signal is recorded yet not acted upon, other continuous changes go untracked, and none of these markers is measured in the individual patient in routine care. Neither clinician nor patient can therefore identify who benefits, who could safely forgo treatment, or who is silently accruing harm. Safety inherits the same blind spot: trials register only what they measure, or events too severe to miss.
One instructive example is rofecoxib. Approved in 1999 on analgesic and gastrointestinal endpoints, it carried none for cardiovascular harm; the signal in VIGOR (2000) [3] was read as naproxen’s protection until APPROVe settled it in 2004 [4].
But these were endpoint-based trials, built to count events, not to measure the prostacyclin-thromboxane shift that would have made the prothrombotic risk visible before it appeared as infarctions. It was not so much missed as never measured: a structural shortcoming of endpoint-based design. Negative results, moreover, are disproportionately unpublished [5], so the evidence base loses the findings that would most constrain prevailing hypotheses.
We regard N-of-1 measurement as the next frontier, enabled by the convergence of artificial intelligence, wearables, multi-omic profiling, real-time tracking, and exposome capture.
Within my own protocols, maximally quantified and self-controlled, we have already generated signals that lie beyond the published literature and constitute first-in-human observations: real-time quantification of heat-shock-protein activation against continuously measured core temperature; sauna-driven clearance of environmental organic toxins (including plasticizers like phthalates and DEHP); enhanced fertility markers under sauna with testicular cooling; a first proof-of-principle demonstration of microplastics complete elimination from the semen and by more than 90% in blood, against a background in which these particles have been found in the semen [6] and testes [7] of every man so far studied; and the first human signal of a psilocybin-induced metabolic switch that lowered blood glucose and durably improved glycemic control.
We treat all of these as observations in need of further validation; our framework is not about finding instant answers but about identifying which questions are worth asking and investigating further.
The paradigm scales. A few deeply instrumented pioneers establish the axes of a high-dimensional physiological space; a larger cohort of cartographers charts its breadth across longitudinal individuals.
Once charted, the measurement burden collapses: neighbour-embedding methods8 position a sparsely sampled person within the map of the high-dimensional individual-biomarker space, inferring state and trajectory from nearest neighbours. This is already routine in biology, where sparse genotyping arrays are imputed to whole-genome resolution against reference panels [9], and, beyond medicine, where roughly 300 social-media “likes” predicted personality better than a spouse [10]. The deep cost is paid once; thereafter each individual is situated cheaply.
The frontier we find most compelling is the unleashing of N-of-1 methods against conditions presently considered incurable. Individualized, biomarker-guided strategies are already converting such conditions from managed to treatable: a documented case of recurrent osteosarcoma brought to sustained remission under an intensive personalized regimen, and, in veterinary oncology, the first personalized mRNA cancer vaccine administered to a dog, which produced substantial tumour regression. The new focus of our protocol is to tackle chronic conditions that current medicine accepts as manageable but not treatable, and to render them treatable through advanced diagnostics and next-generation personalized therapeutics.
References
1. Kent, D. M., Steyerberg, E. & van Klaveren, D. Personalized evidence based medicine: predictive approaches to heterogeneous treatment effects. BMJ 363, k4245 (2018).
2. Ridker, P. M. et al. Rosuvastatin to prevent vascular events in men and women with elevated C-reactive protein. N. Engl. J. Med. 359, 2195–2207 (2008).
3. Bombardier, C. et al. Comparison of upper gastrointestinal toxicity of rofecoxib and naproxen in patients with rheumatoid arthritis (VIGOR Study Group). N. Engl. J. Med. 343, 1520–1528 (2000).
4. Bresalier, R. S. et al. Cardiovascular events associated with rofecoxib in a colorectal adenoma chemoprevention trial (APPROVe). N. Engl. J. Med. 352, 1092–1102 (2005).
5. Turner, E. H. et al. Selective publication of antidepressant trials and its influence on apparent efficacy. N. Engl. J. Med. 358, 252–260 (2008).
6. Li, N. et al. Prevalence and implications of microplastic contaminants in general human seminal fluid: a Raman spectroscopic study. Sci. Total Environ. 937, 173522 (2024).
7. Hu, C. J. et al. Microplastic presence in dog and human testis and its potential association with sperm count and weights of testis and epididymis. Toxicol. Sci. 200, 235–240 (2024).
8. van der Maaten, L. & Hinton, G. Visualizing data using t-SNE. J. Mach. Learn. Res. 9, 2579–2605 (2008).
9. Marchini, J. & Howie, B. Genotype imputation for genome-wide association studies. Nat. Rev. Genet. 11, 499–511 (2010).
10. Youyou, W., Kosinski, M. & Stillwell, D. Computer-based personality judgments are more accurate than those made by humans. Proc. Natl. Acad. Sci. USA 112, 1036–1040 (2015).](https://pbs.twimg.com/media/HLBx6PTbcAA9Gtv.jpg)
It is humbling to consider that if we harness just 1 millionth of the Sun’s power for AI, that will be much more than a million times the intelligence of all of humanity
@JewBizLogic 香港有英國、廣東、紐約、日本和東南亞的影子，是高密度、四不像的縫合怪—— But then 又有哪個國際城市不是這樣的呢？
@yacht_ono Audrey Hepburn did not say that 😂
@sflorimm Recruiters
@0427SMtieshou 第一位是精神上的主體性、獨立性
https://t.co/ZhOgyq7Vgn
One piece of advice we got during YC was to explain our company using verbs instead of nouns.
Early on, I walked into a meeting and did the opposite:
“We’re building a cloud platform for AI”
No one knew that that meant, their eyes glazed over. Then I started saying this instead:
“We containerize your code and run it on GPUs in the cloud so you don’t have to manage the infra yourself”
That clicked way more. Our brains understand verbs because they’re more concrete. If you describe your company using nouns, you risk people not understanding you.
And no one buys or invests in things they don’t understand.
A human brain uses 12-20 watts for core thinking while an AI system doing the same processing could use 2.7 billion watts.
This makes organic brains roughly 100–225 million times more energy-efficient than current silicon-based systems for full biological neural computation.
@bryan_johnson It’s always good to have more data so we can make better decisions and trade-offs whenever circumstances allow.
@JR5_Crypto Lack of optimisable, sustainable systems with compounding effects
First-person perspective; worth reading
AI is acting as a macroscopic catalyst that supercharges whatever a nation's DNA (core structural, cultural, geopolitical strengths, global roles) already was.
https://t.co/jJJZoHassJ
@geoffwoo All seed/ pre-seed roadmaps are more or less lies but whether lies turn into scams depends on the integrity of the key persons behind the decks
@caiziboshi 我自己和身邊人22年私募投進的已經連本帶利提現了。祝大家都賺，沒有接盤俠～
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