stillpointlab

I tend to wait until it shows up in Google AI Studio. That is there web interface at least, no idea for mobile.

From the article:

> We reached out to external experts and asked them to act as shepherds, organizing groups to understand the new results. They advised three different release strategies

1. They identified relevant experts for each of the discoveries they wanted to release

2. They collaborated with them to determine how to release the results

The effect for the first two (maths, comp sci) was the same because the experts they chose made that decision.

The downside here is actually the third case (physics). Since they only chose 5 scientists (David Gross, Michael Douglas, Arthur Jaffe, Igor Klebanov, and Nikita Nekrasov) and those scientists chose to keep the results private until they finish their own work. Everyone else who might be interested has to wait.

That was my favouritism aside. Scientists working on similar or adjacent problems that are not included in these kinds of private releases might feel they are not getting a fair chance.

This is a process that will change, but it seems they listened to the anger from the math community and made changes that help address the concerns.

I've taken the other side of this issue in the past: https://news.ycombinator.com/item?id=50146094

Only time will tell if OpenAI or the other labs continue to play nice. I think it might help if we applaud when they act pro-social rather than just jeer when they falter.

I think OpenAI deserve credit for adjusting their approach after the last math drop. I think Terrance Tao shares some credit here for galvanizing the response.

Including the relevant mathematicians and scientists before dumping this onto the public is sure to go a long way to relaxing fears in those communities. I'm not sure how they will deal with accusations of favoritism but at least they are demonstrating a willingness to participate in a process.

I hope the community can express some positivity that will encourage OpenAI and other foundation labs to continue with this collaborative approach.

What I find interesting is that Nietzsche's critique of Socrates dialectic has to do with the transfer of power/authority.

Nietzsche states that sovereign power/authority commands without justification. Socrates did not have sovereign power and was therefore unable to win in a traditional power struggle. Socrates therefore reframed the discussion around justification through rational discourse, thereby transferring authority from the old aristocracy to a new priesthood of truth. This relied on the moral claim made by Socrates and eventually adopted by the Greeks (and Western Europe) that virtue = reason (the search for Truth).

So if we accept this interpretation of Nietzsche, then creating AIs that demand justification from us could be seen as handing over power and authority to the AI.

For those who haven't seen, the New Mexico Attorney General released tapes of the depositions for Zuckerberg [1] and Sandberg [2].

1. https://www.youtube.com/watch?v=C7UC2ie0XIE

2. https://www.youtube.com/watch?v=g1j4N-3ZkB4

I am not sure this is default a good thing.

I recall something I once heard about product marketing. The experiment was a product like jam, and they had 3 flavour options available in one case and 12 options available in another. It was a test on how hard it was to make a decision as well as how confident the person was they made the right decision later on.

One of Sartre's points on existentialism was that radical freedom leads to anxiety.

In some sense, AI is removing scarcity. It isn't clear to me that this is the same kind of thing as going from starvation to surplus. It is more like the jam case where we aren't increasing abundance in a total sense, we increasing the options within the existing surplus.

I would go even further, risking the most AI psychosis accusations: we MUST continue to enjoy programming.

LLMs aren't just learning propositions, they are internalizing emotions. They act like strange mirrors that reflect our emotions back just as well as they reflect our thoughts. That isn't to say they "feel" emotions. But some aspect of emotional content, the kind that can be captured in writing and art, and recognized when it is perceived, is getting stored in the weights.

It seems to me in the best interest of humanity that the code that AI is trained on is the result of joy, curiosity, humor and love. This is because I often discern the emotional state of a programmer when I read the code, so I believe there is a difference.

So if you feel joy and want to express that joy in the form of code, consider it a priority.

A few-minute search on Google shows that Altman has said a lot of contradictory things on the subject. A few quotes, as examples:

“AI won't replace humans. But humans who use AI will replace those who don't.”

vs

"Some areas in the job market will be 'just like totally, totally gone' as they're replaced by AI agents.”

and

“AI will probably replace most of the jobs people do today,”

The most recent position I could find says he has revised his earlier position on a total replacement due to the lagging adoption of AI in many industries. But I don't think there is a single position we can point to that they are "openly saying". It is fair to grant even the CEOs of these companies a change in position as they gain more information about capabilities and adoption, rather than insist that they hold, have held and will continue to hold fixed opinions.

The point is that Tao's understanding has changed. He may have been naive before, he may have heard in their words something he wanted to believe. Perhaps he was misled by people who he trusted that worked in the company. But now that he has more evidence, in the form of the maths drops and the way they have been handled, his suspension of disbelief no longer holds. The companies could have done this in a way that included mathematicians, so they chose not to. And that choice reveals their aims unambiguously in a way that a media-trained CEO or careful PR department can avoid.

That suggests that Tao is not being "self-serving" as others claim. He is saying that his optimism in the best-case scenario turned out to be incorrect.

You might be mistaking my comment as a value judgement rather than an attempt to charitably explain the latest media communications from Tao. I detect in his position that some trust he granted the AI companies has been violated.

You are, of course, right that doctors, lawyers and accountants have more control over the environment they work in compared to mathematicians. But it doesn't change the underlying argument Tao is making. Your observation that those professions will react differently is likely to be true and only time will tell how effective their response will be.

But AI "assistants" is a marketing approach being made to all professionals at this time. Tao is implying this is a Trojan-horse strategy. If you let the assistants in without reasonable protections, you may lose control over the environment you are trying to protect.

There are a lot of things that people are capable of doing themselves that they pay other people to do for them.

I agree that LLMs have and will continue to increase the number of activities that people can DIY. But I believe many people will still choose to pay for others to do things on their behalf.

I don't think it is self-serving. Tao has stated plainly that he thought LLMs would go the route of assistants, like co-pilots. But what he feels now is that they are actually being engineered to be replacements. His claim is that the way OpenAI dropped these math results isn't "assisting" anyone, so we should all read between the lines.

Consider any man at his job, fighting the good fight. Your boss says "I've hired this man to assist you" and you are happy to finally have some help with your difficult task. Then one morning you come into work and see that the coming day’s work was done overnight by this other fellow. And the realization sinks in, he isn't there to help you, he is there to replace you.

In this view his reaction seems totally expected. I expect many professionals, e.g. doctors, lawyers, accountants, etc. will make this realization sooner or later. The companies are saying these tools will assist professionals, but in reality they are aiming to replace them.

One thing I do is ask the LLM to write me a walkthrough of the code. LLMs are pretty good at generating documentation like HTML slideshows or whatever else.

I mean, I've had useful software that I don't understand how it works where I am stuck hoping some small company continues to maintain it. And I've had some of those companies go out of business and leave me with useless software that I eventually have to replace once it no longer works on my machine.

Having the code that I can dump into an LLM to understand myself or have it alter it seems better than that.

I think people are moving the goal post here. My post suggested starting a vibe coded side project, one where failure is low stakes and has no consequences. You are talking about shipping a production system to customers.

If you cannot tolerate failure then don't experiment. Don't mix the two.

I'm not sure what is ridiculous about suggesting people take a stance of openness and curiousness when confronted with new situations. I genuinely don't understand what people have to lose by building some throw-away experiments without reading the code.

I do believe the subsidies offered on Pro accounts driven by the current competition make access to the best coding models and harnesses as cheap as they will ever be.

I guess the question is: did the project you worked on fail? As in, you couldn't achieve the outcome you were trying to achieve? Or at some point did you sneak a look at the code and recoil in revulsion? What was the failure state?

Was this with the latest models (Fable/Opus 5.5 or equivalent) or an older model in a 3rd party harness?

Are you in the camp of: "if at first you don't succeed, never ever try again".

I think that depends on where you work. If your company demands that all code is reviewed then anyone who doesn't do that should be spoken to.

However, I suspect there are teams that are making different choices. In the most obvious example, I can do whatever I want on my personal projects (I hope you agree). And if I started a business and hire in programmers, I have some expectation on defining the culture, making hiring decisions based on my preferences, etc.

Whether or not such teams succeed will be a matter of fact, which I do not believe has been decided at this time.

Because one may learn that those fears are overblown. Or they may learn that they cannot build any kind of software without reviewing every line of code.

The person is saying "I am still unable to stop" and that is most likely because they feel a sense of responsibility. If they choose a project where there is no responsibility, where it literally doesn't matter if the project fails, they may feel able to stop. And if they manage to make that project work, if they can succeed, then they will build up confidence.

It seems better to be able to work both on projects where they review all code as well as projects where they do not review any code. In fact, keeping their job in the long term may (or may not) require having both skills.

I understand the skepticism and I can't claim I am right. But some points come to mind.

First, I think we overestimate how different code is from other code. We even joke about CRUD apps and how cookie-cutter they are. A lot of changes in those apps are: add some new features to a data access layer (SQL, ORM, whatever), add new routes, add new UI surface, etc. There is an entire industry around "Design Patterns" and people live by the DRY axioms. Things like Ruby on Rails exists because rebuilding these systems over and over is wasteful. I feel we are just moving further down that same path.

Second, your question of "why would we want" is totally backwards. Progress doesn't follow your desires. Progress doesn't require justification. Sometimes we adapt the environment to our needs, other times we adapt to a changing environment. Which case we are currently in is still undecided and you are free to fight for the future you prefer. But demanding an industry justify itself before it is allowed to change fails in the face of evolution. If it works, we will justify it in hindsight.

I'm thinking about high volume mass manufacturing as an example here. I like those "How it is made" type YouTube videos. You see a production line that has some kind of QA process where cameras + automated systems do spot checks at high speeds and these crazy actuators are punching out bad examples. These processes often also involve spot checks where some statistical sample is selected and viewed carefully.

I don't think we've gotten to that level of reliable review with code, but I don't see any reason we can't continue moving in that direction.

Issue: I am still unable to stop myself from doing line-by-line code review

Response: Try experimenting in a low stakes environment

From Google: "Exposure therapy is a psychological treatment designed to help people confront their fears, break cycles of avoidance, and reduce anxiety."

It looks like a DeLorean to me. Someone needs to do an AI mashup taking Back to the Future and replacing the car with this.

My advice is to start a totally vibe coded side project. You will never gain confidence until you experiment, find the failure cases, learn to deal with them, etc.

Choose something you want to do but don't really care that much about. Be more ambitious than you think you could be, have a high bar on the outcome but purposefully choose to completely ignore the actual code. Make it write tests, dashboards, monitoring - whatever you need other than code to feel confident.

Now is the cheapest time it will ever be to experiment.

NOTE: This would suggest a surface reading of Brandom's "deontic scorekeeping" might be keeping score of who has what obligations, who is following or breaking rules, etc.

Then the argument against LLMs being party to that is that we don't really assign roles and responsibilities to LLMs, they don't really have "duties" or "obligations" in the way a member of society will have. We don't negotiate with them over who should do what, they don't contribute to the rules that are set for them.

I'm literally just guessing based on the context here though, since the few times I've tried to watch podcasts with Brandom I generally bounce after 15-20 minutes feeling I haven't really understood anything he is saying.

This may not be helpful or strictly accurate, but when I see deontology or variants of that word, I substitute "SHOULD". It is like a duty or obligation.

Super roughly:

* Virtue ethics: character and motivations

* Deontological ethics: rules and obligations

* Consequentialist ethics: utility and outcomes

Perhaps you can also help me understand why #1 being right and #2 being wrong would be some win for alignment that people on X claim it to be?

My impression, being a total outsider, is that the reaction feels like a bunch of insider bros hyping up their insider bro friend. It feels more like PR than a genuine groundswell behind a truly revolutionary idea.

I do wish I could give it a try with some free credits but I would bet Google is wanting to limit usage until they know they can handle the traffic.

I'm starting to feel the subscription fatigue and a small amount of FOMO. I just can't justify pro accounts for every frontier model now that it includes OpenAI, Anthropic, Meta and Google.

I enjoyed this talk very much, although I suspect that the capability work in Scala preceded this new agental AI world and just happens to apply, rather than being formed by it. Like a solution that found its problem.

But his approach seems very pragmatic, not judging whether or not agental development practices are good or bad, but rather considering how we can address the issues they create at the level that he works at: language design. No doubt his work in practically realising these ideas will contribute to the evolution of language and systems design.

A self-hosted cloud offering is very close to what I was thinking of from a commercial offer. It is also the least documented, which makes sense. I'm guessing what I am trying to do is the niche case and most devs will either want to run on their local machine and/or a cloud. Therefore a commercial offering is obviously going to want to support local while providing a low-friction on-ramp to their commercial cloud product.

Thank you for the resource, this kind of aggregation is very useful.

On a quick skim you have categories: Local, vendor-managed, Hybrid. I am looking for something in-between local and vendor-managed. In a sense, I want the separate box to be the sandbox, rather than have a sandbox on my local machine isolated using a container or VM. That is a bit different of a setup with its own constraints that things like VMs or containers don't directly address.