
cheme & bioe meets ai @intelligenceco | formerly genai @capitalone phd @amarallab @clancygroup
nyc; forged in korea
Joined June 2021
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you can rotate your api key
but you can't rotate your genome.
and also homomorphic encryption isn't going to cut it
a couple of thoughts as we rotate all of our exposed credentials...
there is no more shipping software peacefully.
what we're seeing in cybersecurity today will be what we'll see in biosecurity next year.
all the late 2025 early 2026 tricks and hacks for caching only to get permanent caching wow. this is amazing
I have around 30+ TB of biomedical and genomics data I compiled during and after my PhD. It's part of my AI scientist framework, and the compute/storage bill is a lot for me. is there a provider I could apply for academic credits? I also want to make this available publicly
meta wonder is scoring so high on physics understanding and real-world tasks despite subpar (comparatively) coding perf. where are the RL generalists? I thought coding was supposed to conquer all domains lol
where are the data brokers where you buy the workspace data from companies and then recreate an environment like this to train models that predict the next latent state of a business
spencer 선우 hong retweeted
<대부분 겪어가는 시절인연>
초등학교 단짝: 이젠 얼굴도 기억 안 남.
동네 친구들: 타지로 흩어져서 살아는 있나 모르겠음.
중학교 단짝: 인스타, 페북 검색해도 안 나옴.
고등학교 단짝: 연락처는 있는데 안 본 지 꽤 됨.
대학교: 단짝이라기보다 여럿 무리로 다녀서 애매함.
취업하고 친해진 동료: 일할 때만 붙어 다니고 안 만남.
(퇴사 후엔 더 연락 안 함)
결국 내 인연은 부모님, 배우자, 자식인듯.
i remember trying to model 10-12 cells in orca… this has me speechless
Tackling a 60-year-old challenge in quantum chemistry: making density functional theory scale nearly linearly with system size.
This has huge implications for opening the door to realistic systems that have traditionally been too expensive to simulate.
AI has attempted to accelerate these calculations, but models generally struggle to extrapolate, particularly to systems larger than those seen during training. Unlike text or images, quantum-mechanical training data is extremely expensive to generate.
We present a single unified AI model that performs quantum-mechanical simulations of both molecules and materials in quasi-linear time.
Using a novel Fourier neural operator variant, we learn the underlying Kohn–Sham equation map to produce physics-informed, self-consistent answers.
Unlike prior AI approaches that directly predict chemical properties, our model works through intermediate steps to improve difficult predictions, resembling inference-time reasoning in large language models.
To demonstrate its scalability, we run a self-consistent calculation of a magnesium dislocation with about 80k electrons on a single GPU, which previously required about 7k GPUs.
tensorlab.cms.caltech.edu/us…
@DanishK42 @Caltech
spencer 선우 hong retweeted
Tackling a 60-year-old challenge in quantum chemistry: making density functional theory scale nearly linearly with system size.
This has huge implications for opening the door to realistic systems that have traditionally been too expensive to simulate.
AI has attempted to accelerate these calculations, but models generally struggle to extrapolate, particularly to systems larger than those seen during training. Unlike text or images, quantum-mechanical training data is extremely expensive to generate.
We present a single unified AI model that performs quantum-mechanical simulations of both molecules and materials in quasi-linear time.
Using a novel Fourier neural operator variant, we learn the underlying Kohn–Sham equation map to produce physics-informed, self-consistent answers.
Unlike prior AI approaches that directly predict chemical properties, our model works through intermediate steps to improve difficult predictions, resembling inference-time reasoning in large language models.
To demonstrate its scalability, we run a self-consistent calculation of a magnesium dislocation with about 80k electrons on a single GPU, which previously required about 7k GPUs.
tensorlab.cms.caltech.edu/us…
@DanishK42 @Caltech
spencer 선우 hong retweeted
A feature I would really like in @harborframework is the framework triggering snapshots in sandboxes and letting users automatically restart evals and rollouts from saved snapshots if a run fails in the middle because of transient infrastructure or model API problems.
sagar beat my research interview that took candidates 90+ mins in 45. he’s so skilled and i can’t wait to learn from him!
update:
i'm joining @intelligenceco as an applied ai researcher!
we're on a mission to build the infrastructure for the one-person billion-dollar company.
agents are doing crazy things, but we're only scratching the surface. @intelligenceco is pushing the frontier of what real agent-led, long-horizon autonomous work looks like.
excited to work alongside @spencersunoo and join the research team :)
shoutout to @ndrewpignanelli, @KhareAbhishyant, and @katmcguir3 for moving fast and making this an easy decision.
lfg!
best part of building right now is drafting a huge spec and grilling sol on it while it grills me back. it reminds me of my friends and i whiteboarding together
spencer 선우 hong retweeted
Excited to announce our partnership with @tryramp to offer day-one incorporation for startups directly in Cofounder.
You can now apply for incorporation, an EIN, and bank account without leaving the roadmap!

















