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Any previewers have hot takes? I've really preferred gpt-5.5 over Opus 4.8 for data analysis and scientific software work. It seems much more reliable. Fable is unusable for the type of work that I do (due to guardrails). Really looking forward to trying these new OpenAI models out.

Great article. This is the kind of web design that I like.

I see, very interesting, thanks!

This is a cool idea—I know from snooping on sumbit scripts and node utilization on the HPC that I use at my institution that most submissions leave some compute on the table (and many of them are egregiously bad). I'd probably vote in favor of sending every submitted sbatch script through an LLM (at least for everyone else, I'd would prefer tuning my own usage myself :) ).

Presumably the underlying model here is also an LLM? To what degree is it "fine-tuned", or is it just given a set of tools to build a good picture of cluster usage?

I wonder if the tendency to write short punchy sentences stems from deliberate RL efforts to avoid repetitive, consistent writing? I seem to remember that a critique of early LLMs was that they would produce sentences whose construction was too homogeneous. Would be interesting to know the answer to this.

You could say something similar about biology—just physics behind the curtains, and we understand a lot of the basics. The difficulty comes from complexity, not mysticism.

To be clear I don't think that LLMs are sentient, but the appeal in studying them is similar to biology in that you get to dissect a highly complex system with comparatively crude tools.

It feels like a lot of the folks who occupy the same biotechnologist genre as Venter (George Church, Eric Lander, etc.) can come off this way. I agree fully about the grifty nature of aging and longevity research (mostly because of the target audience), but I also think that you need an element of this willingness to entertain ideas that are borderline crazy to get to their status in the first place. Proposing to sequence (or, perhaps more timely—edit) the human genome would have seemed like a wild idea in the 80s, and yet they were thinking about it.

The end of this short interview with Stuart Schreiber has a similar vibe:

https://www.nature.com/articles/s41570-026-00803-0

Note how interested he is in consciousness and AGI. This is something that he's been talking about for a long time, just formulated differently. You need to be able to temper true scientific rigor with a little bit of wackiness to even think about tackling these big questions.

This looks interesting and I will give it a try. I agree that the space-switching animation is painful.

I don't however think that this will solve spaces on MacOS, for the simple reason that opening new instances of apps is inconsistent and often doesn't behave how you'd expect it to once one more than one space is involved (in my experience, anecdotal).

I've come to peace with the fact that I will never be able to simultaneously experience the productivity of i3 and the necessary evil of MS Office/Illustrator on the same OS. The most important factor in my work is who I work with (rather than what I work with) so I'll remain on the latter train for now.

Would love to read a Tell HN post about the kinds of attacks you are concerned with!

Thanks for chiming in, and for your great work on iNaturalist!

Wow, I didn’t know that iNaturalist was so proactive about that sort of thing. It also sounds like you have a really cool yard! :)

Maybe not, but I'd want to know beforehand either way. And looking through accounts near me suggests that a fair number of users add enough detail to make me think that they don't realize that their info is so public (selfies/profile pictures being the most problematic example imo).

Care to elaborate on this?

I love this app, but it's also a significant doxxing risk especially for the large number of non-technical users that it has. A quick look at the map reveals the home addresses and names of many iNaturalist users in my neighborhood, lots of them older folks that probably don't realize that adding all of the neat wildlife that they see in their backyard (or uploading things they see on remote hikes without any 3G coverage once their phone connects to their home wifi network) is also putting their home address on display by adding a cluster of photos right next to their house that are all attached to their account.

The author wanted it to retain some practical value, hence the discussion of the four “layers” of time—departing from the 12-hour system completely, even if there is a better way to represent time outside of it, would make the clock difficult to use.

Honestly pretty crazy, although that must be the max speed. The carrier was going about 10 mph in this case (per Strava).

This is awesome, and is a great example of the type of funding structure that government orgs (looking at you, NIH) should be offering. Government-backed research is the bedrock upon which the US economy rests, and as science becomes more expensive, we need to support research at the intersection of academia and industry more explicitly.

ARPA-H was a great step towards this goal for public health-focused efforts (-omics experiments aren't going to pay for themselves, at least at first) but a more general funding mechanism has been needed. I think this is a great direction for the NSF, and to be honest it's refreshing to see something like this given the horrible stance that this government has taken towards science (which has been compounded by the biotech bubble/correction).

Can you fold very large proteins/complexes with the large amount of VRAM available on Macs? Ram limitations forcing folding runs to proteins ~<1500 is an annoying nit for a lot of protein folding workflows for me—I'd be curious to see if this helps.

This is a great insight. Any thoughts on how to address this problem?

Not parent, but in my opinion the answer here is yes. I agree that there is a real need here and a potentially solid value proposition (which is not the case with a lot of vscode-fork+LLM-based starups) but the whole point should be to combat the verbosity and featurelessness of LLM-generated code and text. Using an LLM on the backend to discover meaningful connections in the codebase may sometimes be the right call but the output of that analysis should be some simple visual indication of control flow or dependency like you mention. At a first look the output in the editor looks more like an expansion rather than a distillation.

Unrelated, but I don't know why I expected the website and editor theme to be hay-yellow and or hay-yellow and black instead of the classic purple on black :)

This is really interesting. I wonder–would it be possible to listen to an audiobook or PDF at 800 wpm once one learns how to understand the screenreader "language"? Presumably the cognitive load would get heavy if the content were a stream of unstructured prose as opposed to code.

Fun questions! My takes:

1) Sadly there isn't really. There are a few good blogs like Derek Lowe's "In the Pipeline" that centralize news, but no anonymous online forum like this.

2) Google scholar alerts, Twitter, Bluesky, and word of mouth.

3) I think our understanding of biological processes at the mesoscale is about to hit an inflection point, largely through advances in electron microscopy (cryo-ET) and the ability to perform simulations at this scale.

4) Not harder but definitely more messy and progress is less linear.

This is really cool! Any tips for finding poems hidden in a large block of text?

It reminds me of the poem composed from one of Trump's tweets: "O, the Pelican. so smoothly doth he crest. a wind god!" There are lots of other examples on r/othepelican.

Likewise–I pop it on the charger in the shower and occasionally at work if I'm at my desk. The alarms, timers, and reminders access (via Siri) more than makes up for the inconvenience of frequent charging. Notifications for messages and e-mails is just a bonus that sometimes ends up being a double-edged sword. The only downsides for me come on long bike rides, and that it is ugly (and getting too big, coming from a Casio F91-W).

Sorry about the late response, the listing that I bought is this one:

https://www.aliexpress.us/item/3256804264671858.html

I didn't bother to mess around with mounting, I just used command strips to attach the driver board to the back of the monitor and mounted the monitor on one of these clamp-type stands:

https://www.amazon.com/dp/B01MFDQR5D

The heat sink on the board can get warm but nothing to worry about regarding being mounted to the monitor.

This comment hits the nail on the head. Another big consideration with the technology in this paper that hasn't been mentioned in this thread is that it opens up a huge range of possibilities for targeting "undruggable" protein targets. Most drugs are small molecules that bind to sites an (relatively much larger) proteins, thereby getting in the way of their function. Unfortunately the vast majority of proteins do not have a site that can be bound by a molecule in a way that 1) has high affinity, 2) has high specificity (doesn't bind to other proteins) and 3) actually abolishes the protein's activity.

With "induced proximity" approaches like the one in this study, all you need is a molecule that binds the target protein somewhere. This idea has been validated extensively in the field of "targeted protein degradation", where a target protein and an E3 ubiquitin ligase, a protein that recruits the cell's native proteolysis machinery, are recruited to each other. The target protein doesn't have to be inactivated by the therapeutic molecule because the proteolysis machinery destroys it, so requirement #3 from above is effectively removed.

The molecule in this study does something similar to targeted protein degradation, but this time using a protein that effects gene expression instead of one that recruits proteolysis machinery. The article focuses on the fact that cancers are addicted to BCL6. This is an important innovation in the study and an active area of research (another example at [1]), but leaves out the fact that these induced proximity platforms are much more generalizable than traditional small molecules because it's the proteins that they recruit that do all the work rather than the molecules themselves. This study goes a long way to validate this principle, pioneered by targeted protein degradation and PROTACs, and shows that it can be applied broadly.

[1] https://www.biorxiv.org/content/10.1101/2024.07.27.605429v1

There are two semi-connected concepts at play here. Polarization in this context refers to the ratio of neutralizing (i.e. "up" vs "down") spins in a given system. For most nuclei in organic systems like protons, carbons, and nitrogens, this ratio is naturally very small, which is the reason that magnetic resonance approaches like MRI usually have poor signal-to-noise. Hyperpolarization techniques usually involve the transfer of polarization from a source of high ratio, like a free electron, to a relevant target (in the original poster's example, 13C in pyruvate). The polarization in this case is hyperpolarized 13C, which has an "up"-to-"down" spin ratio that is much higher than regular 13C, which makes the signal-to-noise that you get from the pyruvate much higher than it would be otherwise. Tumors love pyruvate so this approach means that tumors will light up like a beacon in your MRI.

The physical rotation/tumbling of molecules in an MRI is also very important, because the strong magnetic field is the thing inducing the "up"-vs-"down" split in the first place, and if the molecular motion is happening at a certain frequency with respect to the external magnetic field there are other interactions that can come into play which can affect the coherence of the nuclear spins (i.e. they can fall out of sync). Thankfully, the rotation of a small molecule like pyruvate is very fast (might higher then the "spin" frequency-a.k.a the Larmor frequenct of 13C at the magnetic field strengths involved in MRI) so the physical tumbling of pyruvate doesn't really come into play when trying to measure its signal. It can be another story for molecules that don't tumble quickly, like the ones that make up tissues, fat, etc.

I solved this with a ~$200 driver board from AliExpress. I love the result because it's thinner than any other monitor that I own and I can swap between my MacBook Pro and my desktop machine (running either Windows or Linux).

Obviously this requires a little bit of tinkering and the end result isn't nicely packaged like a factory Apple product would be, but it only took about half an hour to put together and I haven't had any issues with the driver board yet. And it was way cheaper than a "Retina" display from Apple or LG.

My guess is that this area is much harder to break into–enzymes facilitate challenging chemical transformations by stabilizing high-energy transition states in chemical reactions. These states are usually highly transient and therefore much harder to capture using the structural biology techniques that generate the structural data that AlphaFold and similar methods are trained on. Even though there are many structures of enzymes in the absence of their substrate, I would imagine that the small number of structures for states that represent actual catalytic intermediates would make it difficult for a model to internalize the features that distinguish a good enzyme/catalyst from a bad one.

Another consideration is that most protein structure prediction methods only generate the backbone, and the sidechains are modeled in afterwards. Enzyme efficiency requires sub-A level structural precision in the sidechains that are actually doing the chemistry involved in catalysis, so it could also be the case that the current backbone-centric methods aren't good enough to predict these fine-tuned interactions.

Looking forward to walking to the corner store in my Arc'teryx x Kith exoskeleton. Jokes aside, this is really cool. I hope I won't need one for a while, and look forward to the engineering improvements when I do need one.