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Doesn't that happen with any first-year psychology or med-student, too? You have some basic knowledge of what could happen, which therefore means that it does happen, right now, here. It normally goes away with more experience and exposure, and yeah, some personal growth.
I used to co-supervise a PhD student who suffered from severe anxiety, she was good but her anxiety stood in her way almost all of the time. You could see that she used up 3/4 of her brain just on being anxious, there wasn't much power left to do the actual work to any standard. It's a horrible disease. (and yeah, she was on medication and diagnosed).
I mentioned Densha de GO above, they have a designated controller like that for the Nintendo Switch too :)
It wouldn't surprise me if Japan has its own market for train assets. There's a big community of train simulators! Go to the Kyoto or Tokyo train museums, they have dozens where you step into a replica of a train cab and then drive a photorealistic simulation (sometimes also just film) - the ex-keyboarder for Casiopeia runs a train simulation game company that makes those since the 90s (https://en.wikipedia.org/wiki/Minoru_Mukaiya). There are some Nintendo Switch train simulators like Densha de GO that are only available in Japan.
I'm sure there's a treasure trove of already-built high-quality assets of Japanese trains.
Yes, this has been my experience in the Australian academic system as well: everything is broken and nobody with the power to change it, cares.
China seems to make some strides for their own academics, thinking loudly about moving away from glam publications as career incentives: https://www.ft.com/content/64a811f1-b132-4211-8a8c-2252cf964...
It's supposedly about the worry of leaking state secrets, but it will have positive outcomes apart from that.
They are pretty much the same, yes! Those eDNA providers usually use a wider range of primers (12S, 16S, COI etc.) to capture a range of invertebrates, vertebrates, plants etc. For Covid, since it's only that one virus, you can target only the one gene so it's a bit easier.
https://www.naturemetrics.com/species-detection
https://www.ednacollab.org/industry/
These companies focus on environmental DNA - some are more on the level of local government monitoring, some are for private customers.
Why would I pay 10 dollars per month for music I can AI-generate myself for free?
Good move, it's crazy how many scam calls and SMS I receive in Australia. In fact, if I get an SMS or a call, I just assume it's a scam.
Many billionaires are starting up ocean expedition places, surely they'll want IT. Off the top of my head I can think of OceanX (Ray Dalio), Inkfish (Gabe Newell), RevOcean (Kjell Inge Rokke out of Norway). They're all building or already have massive ships with sophisticated IT and research equipment.
Ah, personas!
https://bufferbuffer.com/using-personas-in-technical-writing...
In user experience (UX) design, personas are fictional characters representing the different types of users who might use a product or service. These personas are based on user research and are designed to help designers and other stakeholders understand the needs, goals, and behaviors of the different types of users.
KPGM et al. are used as political ammo to push through internal changes. Those in power rely on consultancies underlying their decisions (painful redundancies, firings, etc.). Acknowledging that the arguments for these painful decisions was hallucinated will lead to many problems for powerful people, so for now it's best to just try and sweep it all under the rug.
Oh no, that's just coincidence. In proteomics world, a peptide is just a short protein (<50 amino-acids?).
I'd go even further: what happens in biology is antithetical to the way software people think.
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.
The accompanying preprint is interesting: https://www.biorxiv.org/content/10.64898/2026.06.03.729735v1
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.
At my previous work, I was collating somewhat random unconfirmed animal sightings. I also had a separate database of animal occurrence probabilities (species distribution maps). I'm not a statistician but that sounded like a clear job for Bayes theorem: given a sighting and the overall probability of that sighting in that area (species distribution map), and some other assumptions about the noise of the sighting, what is the probability that the sighting actually included that species?
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.
Ah yes, there is a gap between what our regulator wants and what the reality is. I have no qualms that they'll hover out the data if they want to, we know that since Snowden. But I have to comply with the regulator, not with reality.
You can use Claude and Copilot via AWS Bedrock, and there are VSCode plugins for that https://dev.to/aws-builders/setting-up-aws-bedrock-with-clau...
3. from my opportunity - For many (not all) LLMs, Bedrock gives you control over which country the data stays in. You have no control over that with the Claude API, for example. We do not work in the US and have strong requirements for the data to stay in our country, which Bedrock gives us control over.
There has been a bit of a 'trend' to rewrite common bioinformatics/comp-bio into faster languages (Rust) via LLMs, OP's repo seems to be an early example.
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 really like this pattern and use it often, this 'not showing my cards'. The second I hint towards the LLM what I prefer it will become sycophantic and invent nonsense why my preferred solution is better.
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.
Some evidence as to why Brown did not originally win the Pulitzer, instead this citation a few years too late:
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
I work on a few HPC systems with unusual, kinda custom-rolled architectures. A whole bunch of Python and R packages fail to compile on these systems. There's no publicly accessible documentation for these HPC systems, nor for these custom architectures. ChatGPT and Claude so far have given me only wrong advice on how to get around these compilation errors and there's not much on Google for these errors, but HPC staff usually knew what to do.
* For years, despite functional evidence and scientific hints accumulating, certain AI researchers continued to claim LLMs were stochastic parrots: probabilistic machines that would: 1. NOT have any representation about the meaning of the prompt. 2. NOT have any representation about what they were going to say. In 2025 finally almost everybody stopped saying so.
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.
Yes, same in Australia. Keep receipts and add the cost to the web form.
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?
to accurately prepopulate tax returns for around 45% of Americans. (Those other countries have much simpler tax codes than we do.)
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?
If you can, read Robert Caro's The Path To Power (Caro's The Power Broker has been a HN favorite ever since Aaron Swartz recommended it). It's the story of the first ~30 years of Lyndon B Johnson's life.
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.
Related, I think people have stopped.... reacting on the internet? I've been part of the X/Twitter to Bluesky migration and people often mention how 'quiet' Bluesky is.
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.
Bioinformatics has biostars :) https://www.biostars.org/
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.
In my niche, bioinformatics, linkedin has become somewhat of a force ever since many people left Twitter/X during the 'rebranding'? It's quite weird.
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.