We went to bruno years ago and has been good. Nice to be able to store the request definitions in git. But to be honest, don't really need any of these tools anymore.
I used to spend so much time in a debugger with Bruno open. One of my proudest "dad moments" at work was showing the younger members of the team how to connect to the debug port and step through some complicated logic.
Now that, like so many other workflows, is a Claude prompt
Is it easier to teach astronauts to drill or teach drillers to be astronauts?
My money is on Eli Lilly leveraging AI to develop drugs vs Anthropic becoming Eli Lilly. Obviously teaching drillers to be astronauts is the better movie though.
I've found, from working with scientists and non-software specialists at NASA, universities, and various industries, that the big wins come from specialists learning to code as the vanguard, then a few engineers taking over for production. I think that's ever more true with AI assistance, and I'm not sure what is left for product-ization. All I know is it's changing fast.
It's always something I've hated, coming from software and working with experts. I always wanted to be the "software guy" who gets to learn the specialist engineering disciplines. It just felt like swimming upstream and the incumbent specialists always wanted to DIY.
Having said that, some subject-matter experts just don't have the stomach for it. They are so separated from software that they have a block to building even with AI. A good s/w-specialist pair is dynamite there.
> the big wins come from specialists learning to code as the vanguard, then a few engineers taking over for production
The best places to work are ones with high mutual respect between the 'R' and 'D' of R&D. When either camp dismisses the value of the other and everyone stops giving a little to make the handoff easier it just makes everyone miserable.
Yeah what goes unspoken in all these AI discussions is that there's a culture of collaboration that just evaporates when everyone is pushing their own ideas.
There's very little mutual discussion, brainstorming, anything. The slowness inherit in building software before made those discussions almost necessary, now they're post-hoc criticisms (PR "review") rather than fulfilling collaborations.
It's fine from a technical sense, but I do wonder what it does for culture.
"that the big wins come from specialists learning to code "
I used to work at startup that did video encoding. We had tons of computer vision scientists that refused to learn decent C++ coding practices. Their stuff never worked right and they didn't really contribute much. We also had a few scientists who were decent coders and pretty much all progress came from them.
I see the same with some data scientists. They want to do pure data science and refuse to do the coding to actually get their data. So they have to be spoon fed all the time and their progress is super slow.
Look at the background of those researchers: Demis was cogsci phd (and he is a general intelligence that can work on a wide range of scientific problems) and John Jumper has a masters and phd in chemistry. Their advisors and coauthors included some of the best-known ML people who worked on protein structure prediction previously. Personally I think they should have got the biology one, but they weren't unqualified scientifically speaking.
Largely, most of the paper pushed in these big pharma companies is in regulation and compliance. So much so that it isn't that much of a stretch to say they are mostly regulatory compliance companies with a research division attached. It's not entirely true, of course, but it does fit a bit. Also, there are a lot of regulatory agencies out there, not just the FDA.
That regulatory function is quite personal and hands on. It's relationships built up over months and decades. AI, almost by definition, cannot replace it or augment. Sure, faster, better. But even then that backlog will grow to meet any gains.
Source: I have done (and have family members that do) compliance and regulation for larger phramas, including drugs and instruments that you have had (with near certainty) your medical reports analyzed and produced by.
That's my bet as well, but not because Anthropic couldn't become Eli Lilly on intellectual/intelligence grounds.
The regulations, liability, and stringency of procedure in pharmaceuticals and healthcare in general simply won't gel with the SV mindset of "move fast and break things"/"forgiveness is easier to get than permission". A University of Missouri pathologist working with Jon Carreyrou put a SV biotech darling in literal prison despite a huge valuation. That sort of accountability is the exact thing founders seek to avoid, not court.
This distinction has massive implications for Anthropic's valuation. If a pharma company uses Claude in computational drug development, their work product remains their IP, not Anthropic's.
It’s going to have to be a partnership ideally where both companies collaborate and Eli Lilly provides the data and it’s lab resources and researchers from Anthropic do all of the LLM research.
I don't think having the team of PhDs with infinite resources at Anthropic holding the place of drillers in your metaphor holds very well.
Eli Lilly might be the experienced horse carriage operator to Anthropic as the Ford motor company here instead. Or Blackberry when the iPhone was announced.
The biggest advantage Eli Lilly has is in navigating the FDA regulatory process, and at present, US Federal regulations & bureacracy can be pretty easily managed by putting one of Trump's sons or friends on the board or giving a 'donation'.
We need to focus on making the stuff we already have cheaper and faster.
That's where an LLM and robots can come into play potentially. If we can just scan everyone every year at their physical we could prevent potentially millions of deaths. Yea there are downsides but the downsides are all the extra tests afterwards which we would then have to make faster. Then you add extra blood work at the physical too, not just the default panel.
The only people who trust clinicians are people who haven't been through the healthcare system. There's the before time and after time. It changes you. Every field has good people, bad people and mediocre people. Doctors are no different.
Anyway, I would say I actually had relatively positive experience with Thyroid cancer. I had been seeing my Endo for 10 years at that point since I had hypothyroidism. His nurse was on top of everything. Very calm and reassuring during the whole process.
But all my experiences with the healthcare system outside of that were just disappointing. I had RCVS 2 years ago and I was in the ER and the radiologist summarized the report wrong even though he has read it correctly. They were starting me on stroke protocol and giving me these drugs but I had ChatGPT read it and it was like this is wrong. It's supposed to say no significant stenosis in the neck. It actually said significant stenosis in the neck. The dr reported it immediately and the radiologist corrected it. What a shit show. No one could even give me a straight answer on if I could go back to exercising or when I can get off a medication. Every doctor I saw was completely different. Have a few other stories like that pre LLM's. But you have to advocate for yourself. And always double check the doctors with LLM's now a days.
I was with you right up until your last sentence. Absolutely get a second opinion for any diagnosis that you deem warranted, but putting your healthcare decisions in the hands of an LLM is not the route I'd be suggesting to anyone.
No one advocated "putting your healthcare decision in the hands of an LLM." Answer the post that was written, not the one you rewrote in your head to conform to your biases.
Current frontier LLMs are incredibly powerful diagnosticians. They will generally give you the best assessment that can be made on the basis of the information you include in the prompt. So it's important to make sure that information is accurate, and also that it's phrased well enough to allow the model to weigh the importance of each detail.
Unsurprisingly, the same tips are important when communicating with your actual doctors.
I read it more charitably. Double check with the LLM because it is pretty good a lot of the time and might uncover some useful insights or oversights. Don't blindly trust what it says, definitely, but use what you get from it to guide further inquiry from reputable sources.
Nah. I'm permanently jaded from an experience I had as a teenager visiting a doctor.
I don't recall the specifics, but there were symptoms of an ENT infection, nothing serious. I remember being concerned about a white lump on one of my tonsils. The doctor's diagnosis? A stuck piece of rice.
No, it was not a piece of rice. It was a tonsil stone. It happens almost every time I get sick. And I guarantee that if I shared my symptoms and a photo with AI, it would render the same conclusion.
Would it be dumb to blindly trust everything a model says? Sure. Should you trust it alone, especially for major issues? No. But having received such a stupid and brain-dead diagnosis from a supposed "professional" doctor in the past, I would never give them the automatic benefit of the doubt.
Even the great doctors make mistakes. As a patient, you know your history front to back, but doctors can forget or miss even basic details. They tend to have little time for each patient, so I understand how it happens. You have to continually pay attention, correct people and make sure to accurately describe everything you know. Be a pain in the butt, do a ton of your own research, and ask ask ask ask ask.
LLMs don't need to be the dangerous ASI thing themselves for them to still be dangerous. It suffices if they
- destabilize the world just by what they do: making previously very hard things (generating fake everything, manipulating people, hacking, bio-weapons research) easy enough fast enough that our civilization doesn't cope with it and/or
- accelerate AI research to the point where they make the dangerous ASI thing appear years earlier than would otherwise have happened
100% this. I agree, it’s very destabilizing for humanity for the reasons you mentioned. I don’t think we know what the fallout will be yet.
Based on our past inability to handle things well, it could be extremely devastating to a large population.
I would like to think we will navigate into a new way of life to make up for the job losses and other things, but the impacts are just too devastating and we’re just getting started.
They are not going to "slow down" anyway. When has tech ever "slowed down"? The only way to "slow down" progress is by implementing anticompetitive measures, which would benefit those in lead.
A model is just another thing to plugin to a harness. I don't give it much more thought than that. If developers are still caught up on Claude Code, or Codex that's just not a long term thing. It's best to develop workflows locally and in the cloud with open harnesses. I know this will be the future because that's how it worked on every other system that developers use.
Sure there are Microslop and Oracle db users but most of the world we live in is Postgres and Linux. That's why I think most companies will run llm's like that.
That’s not true. Before AI, I have been using Jetbrains IDEs as far as I can remember. Also have been using MacBooks for work since my first job. You don’t have to generalise everything. If a particular specialised tool is good at its job just use it instead of re-inventing the wheel
> good at its job just use it instead of re-inventing the wheel
Exactly why is everyone reinventing a harness every month. There will be Microslop / Oracle harnesses and there will be 1 or 2 open source ones that win.
Even if I accepted that given the new repos: all the new code = "It's all garbage." - I would certainly love to be in a position to observe the new code + metadata = software trends, as software eats the remaining world.
reply