Kyle Roh

Transhumanism is the future

Reminder that there are people with 160-190 IQ who actually intellectually mog Mythos (which probably takes a whole Rubin pod to run), while their brain runs on ~20W. We aren't being bullish enough on the biological substrate. Humanity is still early. It's time to bio/acc

It’s a good list but it’s quite telling that @MartinShkreli names capital allocators first and companies second. Why leave out the true GOATs - drug hunters? Here is my list: 1. Paul Janssen (haloperidol, fentanyl, risperidone, loperamide,…) 2. Leo Sternbach (basically all benzodiazepines) 3. Getrtude Elion and G Hitchings: Allupurinol and Acyclovir 4. Akira Endo (Early Statins) 5. Greg Winter (Phage display, basis of dozens of mAbs)

@soatto Can we make it 100T valuation

@kyle_r0k no

SpaceX is undervalued

@soatto :3

Its on purpose.. more regulations = more monopoly power

O rise, Antichrist, in the name of peace and safety, who shall promote total stagnation and control…

@MTSlive Wow

SITUATION DETECTED: Google DeepMind has released DiffusionGemma, an open model that generates entire blocks of text simultaneously rather than word-by-word, achieving up to 4x faster output on dedicated GPUs.

@bravo_abad Of course — nature never writes code sequentially

No scaling laws for single-cell foundation models: when bigger atlases stop teaching the model anything In language and vision, the recipe has been simple: more data, bigger models, better performance. Single-cell biology borrowed that playbook. Foundation models for transcriptomics jumped from 1 million cells to atlases of over 100 million, on the assumption that scale would unlock the same gains. Alan DenAdel and coauthors put that assumption to the test, and the result is sobering. Working from a 22.2-million-cell corpus, they pretrained 400 models across five architectures (from PCA and a variational autoencoder up to the Geneformer transformer) and ran 6,400 evaluation experiments. They varied not just dataset size (1% to 75%) but also diversity, using cell-type re-weighting and geometric sketching to deliberately enrich rare cell types and transcriptional states. The finding: performance saturates almost immediately. On cell-type classification, batch integration, and perturbation prediction, most models hit their ceiling at roughly 1% of the corpus, about 200,000 cells. Beyond that, adding millions more cells changed essentially nothing. More diversity didn't help. Even spiking in genome-scale Perturb-seq data, to give the models perturbed phenotypes rather than just healthy ones, failed to move the needle. Larger models did score better overall, but they too plateaued early on data. Two points stood out. Simple baselines (PCA, logistic regression) often matched or beat the transformers. And the strongest model, SCimilarity, won not because of size but because its contrastive training objective is aligned with the downstream task. For single-cell data, what you train on and how you frame the objective matters far more than how much you collect. This reframes a quiet but expensive habit. In drug discovery, biotech, and any pipeline leaning on cell atlases, the instinct to keep scaling pretraining corpora may be burning compute for no return. The real leverage sits elsewhere: curating high-quality, task-relevant data and matching the training objective to the actual question you're trying to answer. Paper: DenAdel et al., journal license | doi.org/10.1038/s41592…

@AdamDraper Oh yes

I believe this next wave of Bio is going to be bigger than anyone can imagine. Multiple $10T companies. Maybe even a 100 Trillion dollar company.

@William_Blake Good diligence needs good tequila

My bad. Three shots of tequila the night before. Sorry!

"If you are working with AI/ML, there are probably lines of code in your computer / server that are written by me" he built vLLM

TranscriptFormer brings evolution into virtual cell models, learning from 112M cells + 12 species to predict unseen species, cell, and perturbation states. science.org/doi/10.1126/sc…

Great!! Advances in medical technology should always deserve a standing ovation.. Fantastic results, HR 0.4 in metastatic pancreatic cancer is amazing

The deranged, progressivist UK govt agenda now led to police handcuffing a murder victim instead of the attacker. An innocent victim’s life, according to its govt, is only worth a few lines of apology on X. I’m skeptical if such a govt is worth surviving. May he rest in peace..

We built a joint experimental and computational platform for scalable multi-modal single-cell chemical screens — profiling RNA, protein (including phospho-signaling), and chromatin accessibility responses to thousands of small molecule perturbations in parallel. biorxiv.org/content/10.648…

Only china can pull this shit off wtf