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Still Day 1 for our understanding of 4 billion years of evolution.
If you believe the brightest should run toward the darkest places, join us.
If you believe the brightest should run toward the darkest places, join us.
We can now design proteins. But Nature's most remarkable capabilities still can't be studied or engineered.
The biggest breakthroughs hide in the unlikeliest places. So we're building the tools to climb the tree of life.
You coming?
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Henry Lee retweeted
Please RT and spread the word.
The Bisson Lab (IU Bloomington) is searching for 2 full-time scientists (Postdoc or Postbac) to fill grant-funded projects. For more info, follow the thread and check our website out:
bissonlab.com
[1/3]
The Bisson Lab (IU Bloomington) is searching for 2 full-time scientists (Postdoc or Postbac) to fill grant-funded projects. For more info, follow the thread and check our website out:
bissonlab.com
[1/3]
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Henry Lee retweeted
Creation of light emitting proteins by AI.
AI designed a completely new enzyme capable of producing light through a chemical reaction. The artificial luciferase was only 13.9 kDa, remained stable above 95°C and showed catalytic efficiency comparable to natural luciferases.
doi.org/10.1038/s41586…
AI designed a completely new enzyme capable of producing light through a chemical reaction. The artificial luciferase was only 13.9 kDa, remained stable above 95°C and showed catalytic efficiency comparable to natural luciferases.
doi.org/10.1038/s41586…
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Henry Lee retweeted
Someone made a Linux script that emails Sam Altman every day to report how much unused RAM is sitting in your PC.
Called Ram-Altman, it monitors your free memory and sends him a daily email if more than 10% is unused.
It even includes different email messages and a test mode.
Called Ram-Altman, it monitors your free memory and sends him a daily email if more than 10% is unused.
It even includes different email messages and a test mode.
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Henry Lee retweeted
Ralph Baric is the reason we had COVID vaccines in less than a year. He is an extraordinary, brilliant scientist. I cannot tally the value of his contributions to the field of virology & to humanity. Many people are alive today because of his work. I am privileged to have worked with and learned from him.
journals.asm.org/doi/10.1128/jv…
journals.asm.org/doi/10.1128/jv…
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Henry Lee retweeted
🚨 come work with us! 🚨
We're looking for a Research Associate (London, UK): someone curious and diligent to investigate how philanthropy is allocated in science, and how it’s changing the way that science is organised and funded.
Apply here by the 18th November
science.works/work-with-us/r…
We're looking for a Research Associate (London, UK): someone curious and diligent to investigate how philanthropy is allocated in science, and how it’s changing the way that science is organised and funded.
Apply here by the 18th November
science.works/work-with-us/r…
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‘We told it to stop improvising (“Please let’s do normal things unless they don’t work”).’
anthropic.com/research/inves…
anthropic.com/research/inves…
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Henry Lee retweeted
You cannot fire scientists, halt ongoing research and clinical trials, drive away the next generation and then declare a “golden age” of American science. These cannot fundamentally coexist.
As we head into the elections here is a breakdown breakdown of the damage done to the tem so far:
Defense research labs lost roughly 14% of their staff. The Pentagon itself warned of “potentially grave consequences for the future of national defense.”
NIH staffing fell by 1/5. More than 2,000 biomedical research grants were delayed or canceled, affecting work on cancer, Alzheimer’s and other major diseases affecting the health of American public.
HHS announced a wind-down of mRNA vaccine development at BARDA, affecting 22 projects worth nearly $500 million through cancellations, cuts and restructuring and halting new mRNA projects under the program.
The National Science Foundation lost roughly 1/3 of its staff and withheld more than $1 billion in funding.
The Energy Department lost more than 1,000 scientists and technical staff. Grant awards from its two major science offices fell by more than $500 million during the administration’s first year.
NASA lost 20% of its employees with Ph.Ds.
Foreign Ph.D. applications at leading universities fell 21%. Schools reduced admissions as funding uncertainty made it harder to support future scientists and removing a pathway for sustaining scientific excellence globally.
Meanwhile, a proposed rule would give political appointees greater control over who receives research funding, screening applications for supposedly “anti-American values.”
Thankfully Congress rejected the massive proposed budget cuts and courts forced funding restorations. But neither can replace the time and investment already made. We can’t just simply flip the switch back on.
And while the federal government can still spend roughly $200 billion on research - we continue to witness the dismantling of the capacity to turn funding into discovery.
And make no mistake - other countries are happy to step in - including China - who are recruiting the talent we are losing.
The treatments we will need in 20 years depend on the research we conduct today. So do our security, our prosperity and our ability to maintain our lead globally.
Vote tomorrow because science depends on it.
As we head into the elections here is a breakdown breakdown of the damage done to the tem so far:
Defense research labs lost roughly 14% of their staff. The Pentagon itself warned of “potentially grave consequences for the future of national defense.”
NIH staffing fell by 1/5. More than 2,000 biomedical research grants were delayed or canceled, affecting work on cancer, Alzheimer’s and other major diseases affecting the health of American public.
HHS announced a wind-down of mRNA vaccine development at BARDA, affecting 22 projects worth nearly $500 million through cancellations, cuts and restructuring and halting new mRNA projects under the program.
The National Science Foundation lost roughly 1/3 of its staff and withheld more than $1 billion in funding.
The Energy Department lost more than 1,000 scientists and technical staff. Grant awards from its two major science offices fell by more than $500 million during the administration’s first year.
NASA lost 20% of its employees with Ph.Ds.
Foreign Ph.D. applications at leading universities fell 21%. Schools reduced admissions as funding uncertainty made it harder to support future scientists and removing a pathway for sustaining scientific excellence globally.
Meanwhile, a proposed rule would give political appointees greater control over who receives research funding, screening applications for supposedly “anti-American values.”
Thankfully Congress rejected the massive proposed budget cuts and courts forced funding restorations. But neither can replace the time and investment already made. We can’t just simply flip the switch back on.
And while the federal government can still spend roughly $200 billion on research - we continue to witness the dismantling of the capacity to turn funding into discovery.
And make no mistake - other countries are happy to step in - including China - who are recruiting the talent we are losing.
The treatments we will need in 20 years depend on the research we conduct today. So do our security, our prosperity and our ability to maintain our lead globally.
Vote tomorrow because science depends on it.
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Oooook this is interesting
We asked GPT-6 Astra to edit Qwen3.5-0.8B weights to improve math.
It directly changed only 0.016% of weights, fixing 0.8B's reasoning loops & improving math up to 6x and even instruction following 3x, without gradients or training.
We call this Weight-Space Programming: fronely read/write neural network weights! 🧵
Blog: rikiyat.github.io/blog/weight-sp…Community noteLLM was given weights explicitly as text in prompt and used code/forward passes (per authors' logs) to ablate Qwen looping direction, a known thinking-mode issue per model card. Primarily fixed repetition in that mode, not general 'natively' improving weights/math.
rikiyat.github.io/blog/weight-sp…
github.com/RikiyaT/lm_und…
huggingface.co/Qwen/Qwen3.5-0…
huggingface.co/RikiyaT/Qwen3.…
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It’s Jev, got it.
Carrion crows make decisions faster than monkeys and humans.
Learn more in a new #SciencePerspective: scim.ag/4zmOF1i
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Gonna train my Opus to output text using this corpus
Anthropic has filed an S-1 registration statement with the SEC related to its proposed IPO.
Read more: anthropic.com/news/s1-sec
Legal disclaimer: A registration statement relating to these securities has been filed with the SEC but has not yet become effective. These securities may not be sold, nor may offers to buy be accepted, prior to the time the registration statement becomes effective. This post does not constitute an offer to sell or the solicitation of an offer to buy these securities, nor shall there be any sale of these securities in any state or jurisdiction in which such offer, solicitation or sale would be unlawful prior to registration or qualification under the securities laws of any such state or jurisdiction.
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The Confident Internet Theory
This paper answers why confident idiots run the world.
Cornell University published a psychological paper that explains every terrible boss, every loud influencer, and every broken corporate hierarchy.
It’s called “Unskilled and Unaware of It.”
they tested participants across or to measure the gap between actual performance and self-perception.
And the results are wild.
participants scoring in the bottom 12th percentile consistently believed they were operating in the 62nd percentile.
they were not lying. they were literally incapable of recognizing their own incompetence.
the paper revealed a brutal paradox:
the exact skills required to master a domain are the exact same skills required to accurately evaluate your own competence in that domain.
if you lack the skill, you inherently lack the cognitive capacity to realize you are failing.
this creates a dangerous systemic loop.
the incompetent are blessed with absolute, blinding certainty. their brain protects them from their own ignorance, allowing them to speak first, demand authority, and never second-guess their decisions.
meanwhile, the actual experts suffer the exact inverse.
top performers in the study consistently underestimated their rank. because a task was easy for them, they falsely assumed it was easy for everyone else.
they hesitate. they obsess over nuance. they assume they have nothing unique to add.
we are biologically wired to confuse confidence with competence.
the loudest person in the room gets promoted, gets funded, and gets the microphone, not because they know what they are doing, but because they are physically incapable of seeing what they don't.
the only defense mechanism is building systems that stop rewarding absolute certainty and start demanding proof of work.
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Henry Lee retweeted
Largely agree with these thoughts, and have some perhaps discordant points to add.
- Solving biological diseases in the next few years will benefit mostly from the usefulness of the language models, substantially more than molecular ML.
- Proponents of the bitter lesson for biological models (not LLM) aren't wrong, just (possibly pathologically) too early. We don't have enough data, and the data we can collect quickly is probably not the right kind (we may get lucky with biology being easy, but it's usually not).
- I think just scaling models and public (passively accumulating) data is a cost ineffective direction (it's like scaling dense NNs on the internet). Today's model outputs are as good as GPT2. A single bioinformatician with linear regression, sometimes even without any additional data, can beat these models in many applications (people rarely benchmark AI advances against a simple Potts model). The most promising approaches for molecular models are innovating heavily on architecture (e.g. Baker Lab, Alphafold, etc). This may automate/speed up with LLMs.
- Few companies (hopefully Dyno included) are betting that we've triangulated enough pieces from multiple experimental channels to predict (with scale) how things translate in humans. Having worked on translating in vitro things to primates, I'm very cautious with the idea that current models, trained on current public data, can do anything more than manipulating already working solutions, either explicitly or through implicit remixing of what has worked. It may still work, but the underlying bets offer no specific reason why there is any epistemic risk advantage other than being "made with AI".
- Short term, there is more promise in combining molecular models with LLMs rather than scaling them. The two sources of lift in AI discovery elsewhere has been (1) Breaking Silos: AI synthesizes a bunch of knowledge/expertise that is rarely present in the same room (2) Data mining : AI pushes through volumes of data, with an intelligent eye, that humans or simple algorithms simply can't scale or adapt to. I'm curious what will come out of multimodal training of LLMs and biological data, which seems to be something the big players are pursuing. I think latent reasoning in the language (or better yet, + code) domain will reveal a lot more biology than scaling on nucleotides or proteins alone.
- My view is that the next opportunity of combining lab and AI will be massive-scale hypothesis testing with LLMs. Currently a multiplexed assay generally tries to test a handful of ideas because the humans are bandwidth limited in designing properly powered experiments. LLM's can scale this very well.
I have no doubt lots of good news will come on the impact of AI in biology in the next five years. I can't tell what paradigm will win, but my prediction is that it will not be the same paradigm imported from the LLM land (which will get a ton of funding anyways, because it's easy to pattern match for tech VCs).
- Solving biological diseases in the next few years will benefit mostly from the usefulness of the language models, substantially more than molecular ML.
- Proponents of the bitter lesson for biological models (not LLM) aren't wrong, just (possibly pathologically) too early. We don't have enough data, and the data we can collect quickly is probably not the right kind (we may get lucky with biology being easy, but it's usually not).
- I think just scaling models and public (passively accumulating) data is a cost ineffective direction (it's like scaling dense NNs on the internet). Today's model outputs are as good as GPT2. A single bioinformatician with linear regression, sometimes even without any additional data, can beat these models in many applications (people rarely benchmark AI advances against a simple Potts model). The most promising approaches for molecular models are innovating heavily on architecture (e.g. Baker Lab, Alphafold, etc). This may automate/speed up with LLMs.
- Few companies (hopefully Dyno included) are betting that we've triangulated enough pieces from multiple experimental channels to predict (with scale) how things translate in humans. Having worked on translating in vitro things to primates, I'm very cautious with the idea that current models, trained on current public data, can do anything more than manipulating already working solutions, either explicitly or through implicit remixing of what has worked. It may still work, but the underlying bets offer no specific reason why there is any epistemic risk advantage other than being "made with AI".
- Short term, there is more promise in combining molecular models with LLMs rather than scaling them. The two sources of lift in AI discovery elsewhere has been (1) Breaking Silos: AI synthesizes a bunch of knowledge/expertise that is rarely present in the same room (2) Data mining : AI pushes through volumes of data, with an intelligent eye, that humans or simple algorithms simply can't scale or adapt to. I'm curious what will come out of multimodal training of LLMs and biological data, which seems to be something the big players are pursuing. I think latent reasoning in the language (or better yet, + code) domain will reveal a lot more biology than scaling on nucleotides or proteins alone.
- My view is that the next opportunity of combining lab and AI will be massive-scale hypothesis testing with LLMs. Currently a multiplexed assay generally tries to test a handful of ideas because the humans are bandwidth limited in designing properly powered experiments. LLM's can scale this very well.
I have no doubt lots of good news will come on the impact of AI in biology in the next five years. I can't tell what paradigm will win, but my prediction is that it will not be the same paradigm imported from the LLM land (which will get a ton of funding anyways, because it's easy to pattern match for tech VCs).
There’s a really pronounced divide between people who are on the AI side of things and people who are on the biology side of things. Even among people working in AI, the divide persists based on where they’re focused.
The AI side, adherents of scaling laws and the bitter lesson, believe that AI getting better means AI being more capable of novel discoveries in medicine (that go beyond discovering new drugs in well understood targets or speeding up dry lab work etc).
The biology people think the idea of a bitter lesson in biology is kind of ridiculous and that regardless of AI’s capabilities it’s going to be at least 5-10 years until you could hope to reliably simulate human toxicology in silico (right now AI/ML can’t even reliably simulate in vivo studies like binding affinities or functional assays).
Even the most AI pilled of them, who will list all the areas that AI can reduce the time spent on various areas, don’t think the concept of AI discovering novel targets or generating new understanding of causal biology is realistic in the near term. Most, in fact, will readily characterize the assumption that even an “ASI” level model could find cures hidden in our current understanding (without expanding that understanding with significantly more biological data) as patently false.
This was a big part of why we wanted to experience it ourselves by using AI for a (very) small portion of what it can do in biology. It was both mind blowing and revealed some poignant insights into how different the problem of making AI better at something like coding is to the problems of biology.
Even if it unlocked the ability to generate new molecules at a stunning pace, its simulation (in silico) of binding only had 49% accuracy. And if we wanted to improve that, we’d need to feed it back the data from the lab - data that took about 30x as long to get as it took to generate the molecules. And these were tests in a test tube (turn of phrase), imagine how long it would take if we wanted to improve the model with data that required recruiting humans and monitoring their reactions.
Human biology is very complex, and adding a drug - especially a novel drug and doubly so for a novel drug acting on a novel mechanism - means mapping interactions that have nth-order-effects that we don’t have the data to predict. Virtual cells have made advancements enabled by AI, but the perturbations equate to a small fraction of the possible.
This is before you even get into curing disease, especially uniquely human diseases. Brain function involves cellular identities that are unique to humans, meaning animal studies are of little use.
How long does it take to understand what the side effects of chronic administration of a drug in humans are? AI can speed up analyzing the data, or designing the trial or even getting it approved…but it can’t make a 6 month trial complete in 3 months.
I think its important to point out that significant advancements can be made in AI that contribute to biology, while also being realistic about how much it speeds up something as complex as curing a specific cancer or diseases of the brain.
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Little has been said about the 3rd wave of micro funds by agentic Angels
What's going to be left for early-stage VC once the upcoming IPOs mint hundreds of new superangels who are more technically savvy and have larger investment budgets than the median seed fund?
The adverse selection seems dire
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Henry Lee retweeted
Announcing Ikigai Grants:
Microgrants give early-stage, bootstrapped talent capital. But that’s not enough.
I’ve watched too many grantees become perpetual microgrant chasers, wasting time applying to one $5k grant after another. Worse, most programs encourage this by funding le doing cool things,” with no real conviction behind it.
I wouldn’t be here without patronage from @tylercowen & @jposhaughnessy. But looking back, the mentors & community were worth much more than the money.
So I built the program I always wished existed:
- $50k total, avg. $5k check
- Matched with mentors & community across @RenPhilanthropy , Fulcrum Science, and my personal network of founders, fund leads, investors & scientists.
Cohort 1 backs emerging talent in areas I believe are deeply underfunded and undervalued. Every application is reviewed by me.
Ikigai Grants is a program of @RenPhilanthropy, with support from Fulcrum Science. Advised by many wonderful friends & colleagues including: @AshleyNAbel @RaianyRomanni @NeuroStats Bonnie EJ Maven @idatin @ashleyz413 @JackScannell13
Apply by Nov 9th, 9a PST:
alexaraki.xyz/ikigai
Microgrants give early-stage, bootstrapped talent capital. But that’s not enough.
I’ve watched too many grantees become perpetual microgrant chasers, wasting time applying to one $5k grant after another. Worse, most programs encourage this by funding le doing cool things,” with no real conviction behind it.
I wouldn’t be here without patronage from @tylercowen & @jposhaughnessy. But looking back, the mentors & community were worth much more than the money.
So I built the program I always wished existed:
- $50k total, avg. $5k check
- Matched with mentors & community across @RenPhilanthropy , Fulcrum Science, and my personal network of founders, fund leads, investors & scientists.
Cohort 1 backs emerging talent in areas I believe are deeply underfunded and undervalued. Every application is reviewed by me.
Ikigai Grants is a program of @RenPhilanthropy, with support from Fulcrum Science. Advised by many wonderful friends & colleagues including: @AshleyNAbel @RaianyRomanni @NeuroStats Bonnie EJ Maven @idatin @ashleyz413 @JackScannell13
Apply by Nov 9th, 9a PST:
alexaraki.xyz/ikigai
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