2001zhaozhao

Agreed, the state of social media today is basically a result of market failure.

I think the point of this product is not the quality of the games, it's the accessibility and distribution and the fact that it's free to use.

Big tech already controls the distribution of traditional platforms so they are building AI platforms to make sure they keep control of the distribution of new forms of content, including that for AI games now that they're easier to make than a YouTube video.

But inevitably these products (and the competitors they will inevitably spawn) put us closer to a future of arguably dystopian, AI-driven hyper-entertainment world, even if no one inside the companies meant to do that. I can only hope in that future many people still appreciate human-made games, and even on these AI platforms genuine human ideas (assisted by AI) can still bubble to the top.

As for game quality, these look close to Opus 5.5 one-shots to me, maybe even a little worse.

Roblox has already been pushing AI-generated games heavily.

For what it's worth, I'm thinking of some version of this for my upcoming game's modding tools, although the backend processing will be on a web server unlike this demo, and there is much more of an AI agent focus.

I don't think your take is counter to the article. It is precisely the "founder giving a shit about everything" that will be amplified the most by a capable AI harness, because AI reduces the power of capital (e.g. hiring a lot of good engineers or buying a lot of data or ads) as a moat by amplifying good judgement.

(This is temporary until the AI gets better judgement than humans, then capital will therever be the most powerful moat in a market full of dystopic, consequentialist, incredibly long-sightedly-greedy companies)

> Distribution is one of the smaller ones left, but the personal software trend might eat that too

Being a platform for personal software is gonna be valuable, but it needs a lot of trust. (I have a nonprofit idea around this right now)

Btw, I think distribution might temporarily become less important (because with better AI you can actually pull so far ahead of competitors quality-wise and therefore succeed despite a distribution drawback), but long run it actually becomes more important because of AI persuasion and commodification? If you are the super app then, well, you are the super app

Yep private scientific knowledge is a massive new moat that you can pull if you simply invest in scientific discovery methods in general and throw enough resources and tokens at the problem. I think there are already startups specifically trying to do this. The main obstacle is whether you're actually able to pull significantly ahead of (AI-enabled) public science to make a difference, but I guess the math works out if you're sufficiently AGI-pilled.

I agree this is a kind of data moat, but it's also arguably distinct enough to be its own thing.

You are assuming that getting the AI to generate correct (or accurate, representative) data will be very difficult. I would agree, but I think it will become possible in the long run.

(Edit: alternatively you just use AI to get rid of the need for data to solve a problem, like Jev did for traditional classification models)

I think current incentives definitely go against any efforts to build this. It's very hard to build this and be rewarded for it by, say, investors or your boss, because you can't really prove that your system is non-sloppy while your competitor's is (even if being non-sloppy is all that matters), because by definition your novel results are not verifiable or else the model labs will have already trained it into their model.

But the same is true for high-quality AI systems in general. In general, I think AI model advancements will make the systems easier and easier to build until some small guy accountable to no one but themselvs can build it, and then it will actually be built.

This couldn't be more true. Companies will be driven by AI harnesses (as defined by this article) that automate decision-making and prioritization. In the medium term, may the company with the best harness win.

In the longer term, the downstream impact is massive commoditization of software and invalidation of most existing moats. Data moats are gone if you can simulate the data with AI. Even platform effects can be sidestepped if AI replaces one side of the platform.

In addition, while right now agile startups have the advantage, at some point the balance will start tilting towards whoever has the most tokens (OR perhaps durable moats will trump even near-infinite tokens; we will have to see). Startups have a limited time window to have whatever impact in the world they are hoping to have, or to build a moat that won't be disrupted by AI, but there are few of them left in the world.

The upside is that when there is a lot of commoditization, then the consumer benefits.

Is this like a spring boot for typescript or something like that? Looks kind of interesting

I've personally been using a Phoenix LiveView-like approach extremely successfully, combined with LLM coding. It is extremely, stupidly fast. We are talking about loading an entire multi-window desktop environment in the web on spotty 5G networks in half a second, transferring less than 1MB of data.

I think traditional web frontends might be dead for any applications with servers that can afford to keep server-side state of a logged-in user.

(Notably this includes almost any kind of AI application, because the LLM costs dwarf the web hosting costs anyway.)

And for the stateless ones, there's always htmx

FINALLY, I can support Pi in my orchestrator system that relies on integrating agents with my custom MCP server.

I think there will be a lot to be learned if we can scale up the high resolution measurement methods here to map exactly how brainwaves travel when performing specific tasks.

Maybe they should recruit experienced meditators who can give accurate introspective self-reports to be compared to the measurements, then we would start to have a relatively confident map between brain physiology and psychology. (e.g. X brain pattern in Y region specifically corresponds to the Z step of reasoning when solving the problem)

I'm pretty convinced now that grading take-home assignments is pointless with the prevalence of AI cheating. (There's surely already "innovation" going on targeted at letting people cheat on assignments while staying undetectable.)

It's probably best going forward to grade only in-person proctored exams using paper, or offline air gapped computers in the case of programming courses.

Afaik the usage quota nerf to $200 Pro is true. Twitter is all over them right now. (Also you haven't been able to buy the $200 Pro plan for the last two weeks or so)

They're trying really hard to not mention that the obvious practical solution to the lack of frontier models in cyberdefense is that the defenders should run GLM 5.3 themselves.

Perhaps they should do something like remove dual-use cyber safeguards on older models as soon as open weight models of a similar capability are released.

> It's also in their best interest to do so, because if they don't and these provider hosted agents become the norm, demand for independent inference would drop.

I disagree with this part. AI companies will want to try their damndest to control distribution of AI, so that they can enshittify later.

Consumers conscious of this will want an alternative, of course. Might be niche similar to how Kagi is in search because big tech will always have a AI inference cost advantage + making users the product (extra $ from ads & purchase cuts) + the good old strategy of dumping.

Lock-in is easy with these agents because they NEED all of your data to be useful, and they will continue to learn internally about you.

But ultimately the AI company CAN choose to just make all the data exportable and open source their product for self-hosting. (The mainstream ones won't, of course, they want to lock you in and hide their AI prompts and algorithms.)

I think what is sorely needed is a version of Dots/Muse without lock-in risk but is still accessible to regular people unlike Openclaw.

...did you just describe openclaw?

If the AI they use is reliable, this is actually pretty cool.

> indie game developers have stopped the traditional practice of updating a dev log as a form of marketing and community building because people will point their LLm at it and create your own game before you do

And that's why we can't have good things...

(This is just gonna keep happening more and more until eventually we'll need something like a patent system for ideas)

> be intentional about a process, move the planning artifact to a file, use multiple research/propose/review sessions to dial it in

Yeah, I think we need something like that as well. I am actually working on an virtual artifact filesystem in my orchestrator to enable this. So agents can create a persistent, versioned plan artifact separate from the codebase (maybe a HTML) and iterate it alongside the user, much like what ChatGPT/claude.ai can already do but for a coding agent. Then you'd need to define a process and get the agent to follow it, but that's much easier and mostly a mix of prompt and orchestration primitives.

> exploratory implementation elements

This is a good point, I've ran into a lot of instances as well where my agents in plan mode would like to explore something but can't because of permissions. I wonder if there should be some kind of system like a "experiment subagent" to handle it.

> how people mostly use whiteboard today is: plan -> approve -> agent codes -> use whiteboard to explain the code.

I guess it's interesting and useful for now, but I don't think people are going to work at the code level much longer.

In my opinion current coding agents + automatic review systems are already at superhuman reliability during the implementation phase (as in they will not fail something in the plan during implementation and not tell you about it, so there's no need to look at the actual code beyond maybe a cursory glance). I literally just use plan mode + CC's /code-review in each task so it's not like I'm doing anything special. So I think the main human interaction surfaces to target in the future will be in the planning process.

I was describing T3/Superset which help you manage context switching. Whiteboard is obviously different.

They are usually called orchestrators, sometimes Agentic Development Environments (i.e. IDE for agents) if they are complex enough

This is definitely getting at least some things right about how we work with agents today, specifically that we often work at the architecture level, and we need a better alternative to the current Plan Mode offered by coding agents to efficiently architect software at a high level, which is more visual and offers better back-and-forth incrementation with the agent than simply "reject final plan with X message".

From the website demos i definitely think this is a clean interface, although I don't know how much better this is compared to some simple custom Mermaid format, which the agent can write as artifact files and present to users. Zooming out, this app seems like 1 feature (a MCP with a GUI attached to it) rather than an entire product.

Also, I don't know if asking the agent to write specific code changes into the plan is a good idea. I think maybe that a "plan -> approve -> write code" would let the agent write higher quality code than "plan which contains code -> approve". But maybe you can make it work when combined with some specific prompting marking the code as clearly work-in-progress and subject to change, and that the agent should surface any parts implemented differently relative to the plan to the user, etc.

^ This. Progress will accelerate even in a pacing/slowdown scenario because the slowdown simply reduces the rate of AI progress from extremely fast to merely fast.

For an idea of what a serious AI forecaster expects a coordinated AI slowdown to be feel like for the average citizen, see:

https://ai-2040.com/?choices=plan-a-root#playbook-public-pov

A big issue with the programmed aging theory is that while we already have pretty good evidence from a biological perspective of a programmed aging clock, it is harder to find an evolutionary justification for it.

I don't know how the antagonistic pleiotropy theory is doing. If true it would be a satisfactory explanation from an evolutionary point of view but I recall that it had some problems.

Otherwise I don't find the other evolutionary explanations of programmed aging to be satisfactory, especially when you consider that genes are selfish and act on the individual rather than population level.

Perhaps reproductive aging (egg quality) is the ultimate limiter, and then overall aging is adaptive downstream of that because hunter-gatherer families benefit (in terms of evolutionary fitness) from fewer old people to increase the percentage of people at reproductive age? That would fit the modern evidence of early egg freezing significantly increasing reproductive success in older women, showing that the problem of fertility decline by age is primarily cellular.

Or perhaps the aging mechanism is leftover from pre-human ancestry and there simply wasn't enough time in our evolutionary history for humans to evolve longer lifespans, maybe because it's slightly helpful but not nearly as helpful as other traits like intelligence, and lifespan is hard to improve via genetic adaptation due to bottleneck effects?

Isn't it better to do inline code comments, e.g. JavaDoc?

This Luna release might potentially be a big deal for computer use automation at scale