The HN/YC crowd generally has software brain: https://www.theverge.com/podcast/917029/software-brain-ai-ba..., "when you see the whole world as a series of databases that can be controlled with the structured language of software code". Biology doesn't work like that most of the time, it's squishy and weird and unpredictable, and the models we have of biology (including genomics!) are faulty at best, misleading at worst. I've supervised PhD-students and it takes some time for people's brains to be comfortable with that squishiness, that random behaviour, that 'putting A into the system only rarely produces B and we don't really know why but we do it anyway' view of the world. Software engineers struggle, even abhor that kind of world, which is why you rarely see them being interested in it; and if they work in it, outcomes are sometimes amazing and Nobel Prize worthy, more often nonsense that silently disappears.

Modeling protein-protein binding is still a massively unsolved problem, mainly because we don't really have the data. Alphafold2 was great but didn't actually 'solve' protein-folding as all input data is from single 'state' X-ray crystallography of the proteins, not 'really' how these proteins behave in the wild. So it's still very, very had to predict what binds to what, which of course is a multi-billion-dollar industry.

I work in a pharma-field and I wish we could easily design molecular binders. We still spend millions every year finding targets that could 'smuggle' our drugs into cells.

Some other players in this field are Boltz Lab and Isomorphic Labs (the Alphafold Google spinoff led by Hasabi). None of them can predict anything complex or 'big', everything is peptide-level. OP's work is another step towards something better.

The most interesting part in the preprint is that they find no matches for their designed binders in the world-write protein database. An open question with protein-designers is whether they just regurgitate training material, which is far easier to test with English-language models.

Claude asked me three questions and then wrote a beautiful Python implementation that queries the map and spits out a table of adjusted probabilities. Felt immensely powerful - I can do this 'on my own' now, I don't need to wait to find the right people or learn the right thing first.

Seqera Labs has a bit of a manifesto: https://rewrites.bio/

Heng Li has an overview here too: https://lh3.github.io/2026/04/17/the-ai-rewrite-dilemma

IMHO it's... OK? Bioinformatics code quality is generally poor, untrained biologists writing functioning code that is poor in scoping, but works. (Unguided) LLMs write on that level, too, so not much harm done.

I'm sure there's an interesting study on how users 'leak' their preference unintentionally to the LLM; perhaps when users list their options, they often put their prefered option first; but not showing the cards on my hand has been very useful when thinking through a problem with LLMs.

>Brown’s “Perversion of Justice” series won a prestigious George Polk award. The Herald entered the Epstein series for a Pulitzer Prize that year, but it was not a finalist. Alan Dershowitz, the attorney and television personality who helped broker Epstein’s original deal, wrote a letter to the Pulitzer committee that year, urging them not to honor Brown’s work.

https://www.inquirer.com/news/pennsylvania/julie-brown-pulit...

The rot runs deep

Man, Antirez and I walk in very different circles! I still feel like LLMs fall over backwards once you give them an 'unusual' or 'rare' task that isn't likely to be presented in the training data.

They have simplified it nicely, though: if you work from home you can claim a per-hour deduction so you don't have to do the math of wear-and-tear, electricity, internet etc. I think it was $0.6 per hour?

One should note that the cited study quotes the 45% from a 1992 study. These days, with gig economy and quasi-self-employment, that number is probably higher since you don't have an employer who reports your income for you.

Still, here in Australia, where we have the return-free tax system, adding what you earned from your various gig jobs isn't too hard: you add that as items to the web form: 'I made 15,123 from Uber Eats'. That just gets added to your overall return. I don't see how that's so hard compared to the US?

I forget which chapter it is, but Caro takes a detour where he describes the life of women during Johnson's childhood in the dirt-poor valley he was from: no electricity, no waterpower, everything in the house was done by women's hands, 24/7. There's a passage that stuck to me about how women in their 30s in that area looked like other area's women in their 70s, just a brutal life.

I think that's not due to algorithmic intervention of product design etc., I think people are just tired. The novelty of shouting at strangers on the internet has worn off - how many internet fights have we gotten into that did nothing in the end except waste time? It's only worse with a coin flip's chance of the other person being an LLM. We're all tired.

The difference to linkedin is that biostars has 'in-domain experts' only; the postdocs, the staff bioinformaticians, etc. those are not the people who will hire you. The people who will hire you are on linkedin.

They're mostly posts announcing new packages etc. but there seems to be more bioinformatics-y activity than, say, mastodon or bluesky. The posts definitely have a different tone than what OP decries.

I'd say I'm 50/50 Python/R for exactly this reason: I write Python code on HPC or a server to parse many, many files, then I get some kind of MB-scale summary data I analyse locally in R.

R is not good at looping over hundreds of files in the gigabytes, Python is not good at making pretty insights from the summary. A tool for every task.