They claim it's not an LLM, which I read as "not an auto-regressive token generator". I assume they are still using a transformer, otherwise they would be talking about the thing that's not a transformer, instead of all the fluff on the linked page. But they emphasize parallel generation, so is it like a text diffusion model?

Democracies require conserved stake to function. How individuals initially get that stake is a degree of freedom, but if the stake can be printed freely, the system is unstable.

For working implementations of democracy, look at: invite trees, shareholder corporations, proof-of-stake distributed ledgers, etc.

VM, or even just a container will do. The agent should be able to run as root in its environment and do whatever it wants. If you can't give it that, you aren't sandboxing correctly.

This is also the democratic way, arguably more democratic, it's just probabilistic. Juries are supposed to be randomly sampled from the jurisdiction's population (a kind of democratic process). The ability of a jury to judge the law, and not just the particular crime is a feature, not a bug of the justice system. This is how the system is set up, and it is as legitimate as action through a legislative process.

EDIT: the parent asked a legitimate question in good faith, there is no need to downvote them because you disagree with their implied current understanding of an issue.

It's good to see individuals on a jury acting according to rational self-interest.

Apparently superfluous descriptions, unnecessary words, and paragraph footnotes are all fine, but gumbel softmax (or whatever's going on here) isn't.

Here's another way of framing things that might encourage better discussion: Suppose in a few years, it was announced that an LLM did discover a cure for a cancer. What approach would you expect it to have taken?

1. Developing an intervention for every possible mutation that leads to loss of cellular identity, and uncontrolled growth.

2. Preserving immune function later into life.

3. Preserving genetic and epigenetic accuracy later into life.

Maybe it will be 1. But I would bet on 2 or 3. Which both assume that by reducing the difference between rates, the phenomenon of cancer could be reduced or eliminated.

A more sophisticated view is that cancer (one idea) is an problem of rates (like most things in biology) and there is a tumor burden per unit time from whatever causes vs. the immune system's capacity to detect and destroy them per unit time. As humans age, the balance tips in one direction. If the balance is too far off for too long, tumors accumulate, doctors take notice, and assign whatever labels to the condition.

Looking into particular causes of tumors (and calling each cause its own cancer) can create a vast research program that employs a lot of people, since there are so many mutations that lead to tumors. But interrupting the development of a particular mutation is not going to have the same society-changing effect that solving the fundamental issue of rates would.

The people who notice broken or degraded behavior (devs and power users) and conceive of good designs are not in charge of what goes into the product. So you shouldn't expect it to get better, only to continuously change so that the imposters can point to a difference that they were responsible for, knowing that upper management also has no idea whether that difference was actually an improvement.

The solution is to gut your product org, replace management with leading engineers, and hire some of your most fanatic users. They don't need to do anything other than give their opinions, and use the product every day. Put them in a room with your top engineers, and the software will mysteriously get better.

All of this was obvious decades ago, but now everyone wants to have a do-nothing job at a tech company, and to help their friends get one too. The result is imposters everywhere; "imposter syndrome" was brought into the vernacular to normalize a lack of expertise. "No one knows what they are doing, it's okay to not know how to do your job". That's what your (product) manager tells themself in the mirror each morning. Turns out: skill predicts quality, and an org ships quality proportional to the skill of the decision makers.

Whatever you want to call it, HN has followed a similar trend. It's rare to see the authors of small, but interesting/innovative, projects show up in the comments, surprised to see their work on the front page. That used to be common, even the default, if you look far enough back.

Now the front page is current events and marketing campaigns. I don't think I've seen a single software project here in the last year that wasn't already extremely popular, or being pushed by a company with a marketing budget.

In theory AI should have helped. I know people are still making cool stuff, faster now with AI, but it's harder and harder to find it.

The problem that I imagine you see with this, is that it doesn't conform to a particular, special, notion of fairness that you think the market should have.

Informed parties have an edge over uninformed parties. This edge is "unfair" if you believe the market should be a lottery. The market is designed to pay people with accurate beliefs, by taking from people with inaccurate beliefs. Everyone's belief is valued based on its accuracy, and the market is fair in that sense. Fairness is actually irrelevant to the societal good the market provides, which is to produce accurate prices. A third party, who doesn't participate, shouldn't care about the market being "fair", they should care about it giving good information.

> A similar issue is that of market manipulation, since many markets in these platforms can be directly manipulated by participants in manners as easily as spamming some words on an earnings call.

If you are betting on what a person will say, and the person knows about the market, that is a chaotic system. If you bid the price away from max entropy then you deserve the outcome.

This is a common take on "inside information", but for most people this opinion is totally unaligned with their own goals. The people who benefit from "no insider trading" in any market, are a small group of active traders, some institutional, some not.

For literally everyone else, insider trading is a net win. Insider trading improves price discovery. If you passively invest then you benefit from the price being more accurate when whatever fraction of your paycheck goes into the market.

I don't know your own situation, maybe you are one of those few traders who needs information to spread a certain way in order to make money. For everyone else, don't be fooled into promoting an idea against your own interests.

What I mean by that is: after reading through a brief description of a project, or a conceptual overview, they are no better than noise at predicting whether it will be worthwhile to try out, or rewarding to learn about, or have a discussion about, or start using day-to-day.

Things on the front page used to be high quality software, research papers, etc. And now it is entirely driven by marketing operations, and social circles. There is no differential amplification of quality.

I don't know what the solution is, but I imagine it involves some kind of weighted voting. That would be a step towards a complicated engagement algorithm, instead of the elegant and predictable exponential decay that HN is known for.

I think there are people who want to use software to accomplish a goal, and there are people who are forced to use software. The people who only use software because the world around them has forced it on them, either through work or friends, are probably cognitively excluded from building software.

The people who seek out software to solve a problem (I think this is most people) and compare alternatives to see which one matches their mental model will be able to skip all that and just build the software they have in mind using AI.

> And that there is little value in reusing software initiated by others.

I think engineers greatly over-estimate the value of code reuse. Trying to fit a round peg in a square hole produces more problems than it solves. A sign of an elite engineer is knowing when to just copy something and change it as needed rather than call into it. Or to re-implement something because the library that does it is a bad fit.

The only time reuse really matters is in network protocols. Communication requires that both sides have a shared understanding.

I can't think of a single news outlet that isn't obviously propaganda or virtue porn. People who want that can have it, but the internet has has made the business models common knowledge, and now most people would be embarrassed to consume these outlets.

All of that was true before prediction markets, now if a fact is important enough, it has a market, and if I need to know the best take on that fact, I will look at the market.

In the medium run, "AI is not a co-worker" is exactly right. The idea of a co-worker will go away. Human collaboration on software is fundamentally inefficient. We pay huge communication/synchronization costs to eek out mild speed ups on projects by adding teams of people. Software is going to become an individual sport, not a team sport, quickly. The benefits we get from checking in with other humans, like error correction, and delegation can all be done better by AI. I would rather a single human (for now) architect with good taste and an army of agents than a team of humans.

The target does matter, that is the basis for the whole technology, and the thing most predictive of efficacy. That's why the flu shots often don't work and the shots for smallpox and measles do, the flu is a more rapidly mutating target.

Going crazy with the adjuvants was popular during the pandemic when it became clear that the virus had mutated (the target protein), but no one wanted to do R&D for a new target. Counting white blood cells became a proxy for efficacy, and you can manipulate that stat with adjuvants.