anthuswilliams

Pharma does often have some incentive to develop treatments for rare diseases. I worked at a startup where we spent years trying to advance a candidate with an addressable patient population of only 400 people. Such drugs receive favorable regulatory treatment from the FDA and the small number of patients is not a problem because pharma can still make its money by charging an exorbitant amount for the treatment. (Which is of course its own problem, but I'll resist the urge to digress).

The bigger issue is that those programs are riskier, largely because: a) the small number of patients makes it difficult to develop a clinical trial as it is so difficult to find patients to enroll. They have a range of comorbidities and other confounding factors that are hard to control for in such a small trial. And they are spread out geographically so you have to setup and qualify a lot more trial sites and potentially deal with concomitant manufacturing/delivery issues, multiple legal regimes, etc b) the trial itself is harder to develop because there may be no good clinically validated biomarkers you can use for efficacy endpoints. These means you fallback to universal biomarkers like the patient's subjective experience of pain and the rate of death/disability, which in turn means the trials take longer to run.

I'm optimistic that some of the recent moves in the space such as the FDA's Plausible Mechanism Framework for personalized treatments will help here. I see no reason why we can't have clinical trials with a single participant.

Anyway, I hope that Tranberger's work helps find something that will help your wife.

It's also the case that preclinical candidates developed this way are even harder to translate into human trials, because regulators and drug developers don't know how to build a safe and informative trial given the novelty of the methods that produced the candidate. So you essentially end up redoing a lot of the work that the AI allowed you to skip in order to do the work that remains. Perhaps a future innovation in "explainable" AI could help here, but that such a technology would itself need to be clinically validated in order to solve this problem.

Source: spent 9 years at a AI drug discovery startup. We advanced 8 clinical candidates and every one of them ran into some variant of this issue.

I agree with your wife, and I would also add:

d) elderly and infirm people are in a weakened state and so are less likely to recover from accidental complications your drug might introduce

e) sickness is itself a confounder for the toxicology effect of a drug. Suppose you take a drug and your liver shuts down - did the drug cause it or was it your underlying illness? This can lead to programs being prematurely shut down and actually prevent the development of treatments that might have gone on to succeed.

This is one of the laziest and nonsensical press releases I have ever seen attached to a scientific paper. Everything in the press release is either misrepresenting what is in the linked paper or is completely obvious and uninteresting.

Evidently if you fan-out data analysis work across 1946 agents instead of just one, the analysis is faster and more thorough than if you instructed a single agent to go through all of the 1946 genes sequentially. And that, in turn, is faster than if you did it yourself. Like, duh.

The linked paper is not exactly transformative, but the impression it leaves is quite a bit better than the nonsense press release.

When I interviewed at Instructure in 2013, I used this exact story. I got a very similar question: you had a string coming across the network and you were supposed to keep track of how many times you saw each character. I ended up going through it the same way the author did, where you maintained a hash and incremented the count as you read each letter. Then at the end I did a "by the way I saw this blog where someone was discussing this problem and they had a crazy idea..."

The prime number thing is obviously bad for this problem, since the giant number would grow arbitrarily large and you'd have to do a bunch of expensive division operations. But it's out of the standard box enough to suggest to the interviewer you are a curious sort who likes to think about novel data structures. At any rate, I got the job.

In this case, that seems to be exactly what the parent commenter is doing.

One interesting quirk of the AI-written READMEs these days is how they can include every detail on how it works, thoroughly document every optional flag, known limitation, experimental result, and still not communicate the essence of the project and the problem it solves.

I have read through the project and I still don't understand what this thing is for and why it is to be preferred over the harness's native memory management tools.

This cannot be the case. The author repeatedly propounds his worry that the AI is taking work from humans. But if the promptor is carefully prompting it, it's not a case of an AI obviating humans; it's a scammer pitching bullshit to an innocent victim.

Now, maybe one could argue the _promptor_ is taking work from well-deserving professionals by means of this ruse. In which case, call out the lie!

This is important! The reason the AI lie exists is because credulous worrywarts believe the doom scenarios. If AI were recognized as the tool it is (+10% productivity for some domains but not others, larger increase in spam and other nefarious actors) we wouldn't be in the nascent financial crisis in which we currently find ourselves.

It says in the tweet, $1 = 1000 tokens. It is EXTREMELY far fetched; few people who have used LLMs in any capacity would be able to achieve this outcome, let alone a self directed agent.

I've developed in this space. My skepticism is born of my own experience. What really happened is this: the owner carefully scoped agents to adopt personas, carefully vetted their "leads", and if any had succeeded, would have carefully moderated the outcome. Maybe even hired professionals like OP in the process!

What the world must realize is" all the talk of AI agents doing societal damage in pursuit of well-intentioned goals is nonsense drivel propounded by large AI labs. They do lots of damage, to be sure, but they are carefully directed to do so, by scumbags. Look for the asshole prompting the thing; I guarantee you will find more culpability than they would have you believe.

For all his outrage, the author credulously swallows the most toxic and implausible claim iLands is making: the idea that an AI agent, threatened with the pain of "Deep Rest", independently considered a potential career for itself and chose to pitch him on it, all in under 10k tokens.

The fact that LLMs have improved substantially in the last year does not, ipso facto, mean they will continue doing so forever. Nor does it imply, even if they do keep improving, that they will displace human judgment and decision-making and that they will reach a point where they have literally zero limitations.

I don't agree with _never_ outsourcing decisions to an LLM, but the parent is right to point out the importance of thinking critically instead of blindly trusting LLMs. If only to understand their biases and common failure modes. The 1920s calculator metaphor is apt because practitioners using plugboards and Hollerith machines would have been avid early adopters but still keenly aware of the limitations of the device. They used that knowledge to decompose problems into units the device could solve, and to sanity-check the outputs of problems based on their understanding of mathematics and the capabilities of the machines.

I am often amazed at the dismissiveness of AI skeptics given the remarkable results we have seen in the last few years. And the stunning inefficiency of major LLM models and inaccessibility of underlying data mean there are a lot more gains to be had. But despite that, I'm even more flabbergasted by those who claim that AI superintelligence is a thing, that LLMs of all things will outcompete humans on creative and critical thinking tasks. It's a naive extrapolation at best and is completely unsupported by any coherent model of technological evolution, economics, game theory, or human history.

But all that said, I'm of course biased. I am of the belief that humans can do things that machines will never be able to no matter how advanced they become. A quasi-religious belief? Perhaps. But AI boosterism just as indefensible.

Allow me to correct the headline for you:

> Multi-millionaire Netflix executive, known for profanity-laden tirades and shotgunning beers at corporate events, claims without evidence he was terminated for medically sanctioned ketamine use

I should think the point was obvious. It's not a percentage of behavior of a single participant, it's in aggregate. The point is 90% of lobbyists are not doing anything untoward.

That's literally what 90% of lobbying is. Powerpoint presentations to Congressional staffers, econometrics research publications, policy panels and conferences, and so on. Not everything is campaign donations and super PACs.

I had the same thought, but it appears they are explicitly asking about poets, not just literary figures and authors.

Poe seems like an obvious candidate no one listed.

Why not? There are perfectly pro-social reasons a person might ask such a question. And a bio-terrorist would have many other avenues to answer these questions.

The concept of a bioweapon attack is commonly cited but in fact there are many other defenses against actually developing and deploying such a weapon. It's commonly portrayed as easily achievable but is in fact very, very far from it. Moteover, although there is no evidence that AI makes it easier in any way, somehow this is constantly invoked as an example of a thought crime that must be forestalled at all costs.

In the AI era people seem to want to redefine the criminal act from "doing the thing" to "asking or investigating how to do the thing". Not a mindset I share.

It's better to read such essays as fables rather than comprehensive world models. It causes you to look at social and organizational structures in a new and (putatively) helpful way. It should be complementary to the rest of the frameworks with which you evaluate the world.

You still have a DRI. In factories this would be a foreman; in software teams this could be a team lead or product owner or whatever. Their job is to apply the statistical process controls and the gemba walk, to help the team see the problems and develop the causal mental model for why the problems happen. They hold the team responsible, together, for combatting the issues so uncovered. They know who is not pulling their weight.

Of course, to do that, a business, and in turn the DRI, would have to empower the team to act in its business's best interest and stop micromanaging them.

I have been championing this mindset since well before LLMs. It is an admittedly controversial opinion, but one I hold strongly.

Code reviews are a productivity tax. No truly effective team would rely on them. The fact that so many software teams view them as indispensable just shows how few effective software teams there are in our industry.

They are akin to a quality check step in manufacturing. Part of what Deming did in revolutionizing manufacturing was eliminating the step in favor of a holistic quality metric owned by all participants and enforced with rigorous statistical process controls. As you say, we in the software industry have all the pieces (autoformatters, tests, benchmarks, etc) to operate this way, but it seems our organizational and management dynamics combat this shift at every turn.

Relevant: When this conversation comes up at work, I like to share Avery Pennarun's post about the review tax: https://apenwarr.ca/log/20260316

I read the comment as arguing that leadership is necessary, not that it is sufficient. That is, that no amount of governance could have prevented the change in the price of the hot dog; only the leader could.

Enterprise agreements are billed at the API rate (albeit sometimes with committed spend discounts). There is no equivalent of the Max subscription in this context.

I'd also add that top-heavy engineering organizations are sometimes incapable of delivering anything useful because everyone wants to work on the hard problems, establish the frameworks, define the processes, and so on, and no one wants to operate the damn business. It's good to have a mix of perspectives in a team.

At 90 seconds per resume, that would take up a full 8 hour day. Having gone through this myself, I don't think it's possible to do this much faster than that, even if you have an ATS that optimizes for that workflow.

I often found myself falling into patterns of poor judgement, e.g. mentally filtering out resumes based on the layout because, to my tired and bored mind, they looked similar to the resumes I had seen from unqualified candidates. I actually think some automation is helpful in evaluating them more rigorously.

That sort of messaging has been done for decades with business process orchestration companies, RPA vendors, etc. All the way back to the original business software vendors like Lotus and Excel. It's only big LLM labs that adopt this tone of dismissive trivialization of other people's work.

The issue is not that they are generic. You could still be generic with phraseology that actually acknowledged the contributions and ownership involved in the jobs being done. For example, you could write e.g. "monitor for outages", "manage projects", "arrange community events", "handle logistics", and so on.

But the problem is LLMs can't do those things. All they can do is "edit files" and "send messages".

Without commenting on the product itself (I haven't tried it), the marketing copy around this release commits the same sins I have seen from Anthropic and Grok and all the rest of them.

I'm so tired of seeing these companies trivializing other people's work! Nobody's job is "edit files" and "respond to messages"! People have jobs like "find and close leads" and "reconcile accounts" and "arrange student field trips" and "make sure the hospital has enough inventory", not "generate reports" and "write code".

Editing files, producing reports, even writing code is just a byproduct. This is like the idiotic "lines of code produced" metric, but now they apply it to all of society.

> Why is it always our burden to learn and improve

That's what the 400k/yr is for.

I literally _just_ put up an announcement on our internal Slack of a tool I had spent a few weeks trying to get right. Strange to post the announcement and, literally the same day, see a better, publicly available toolkit to do enable that very workflow!

I'm also using Playwright, to automate a platform that has a maze of iframes, referer links, etc. Hopefully I can replace the internals with a script I get from this project.

There is something up with your hiring system. I shared this post with a friend who applied, and they got an instant rejection. This is a data engineer with 4+ years experience in your stack and a degree in nuclear engineering to boot. Maybe not an ultimate hire but clearly not someone you should be auto-rejecting.