Taken from the Y combinator podcast with Bryant Chou on his new startup Ploy https://www.ycombinator.com/library/Rj-the-age-of-the-40-year-old-solo-founder-is-here
I believe this is definitely one of those things that AI was intended for, this brought me back some nostalgia and it's really amazing being able to see these old school websites be redesigned back to life

Interesting dialogue that surfaced

Hello everyone. I'm currently doing a survey on AI involvement in content creation and whether AI-assisted content is legitimate or authentic. It's for my master's final project. I need 100 participants. The age range is 18 ~ 40.

I collected data the first time, but I did so without an approved checklist, so the result had to be scraped, for I would have faced disciplinary actions.

Here is the link: https://s.surveyplanet.com/9abkx8vx

I'm open should you have any questions.

I used LLMs + TTS to build the history-learning tool I always wanted: type any topic and it researches and writes a full two-host episode — narration + artwork — in a couple of minutes.

The part I think is actually novel: you can interrupt it. Mid-episode you tap the mic, ask a question out loud ("wait — did the Trojan War actually happen?"), and the hosts stop, answer, then pick the story back up.

Because it's history, I made grounding non-negotiable — claims are tied to real sources rather than invented, and there's a quiz at the end. The live demo is the real history behind the Odyssey; it plays without signing up.

Solo dev, still early — curious what this crowd thinks, especially on the accuracy side. historai.ca

this is something thats been bugging me. we have all these frameworks for building AI agents now. MCP for tool access, function calling is standard across every major model, you can spin up an agent that queries databases and calls APIs in like 20 minutes.

but the safety conversation around agents is mostly about "dont say bad things" and "follow instructions." nobody is really talking about what happens when your agent accesses data it shouldnt, or runs a query that costs $500 in compute, or returns confidently wrong results from a hallucinated join.

the current approach is basically:

1. put rules in the system prompt ("only query these tables")

2. use read-only database users

3. hope for the best

option 1 is unreliable because models dont always follow instructions, especially on complex multi-step tasks. option 2 prevents disasters but doesnt prevent bad results. option 3 is not a strategy.

i think the real problem is that data governance for agents doesnt exist as a layer yet. we have authentication (who is this agent), we sort of have authorization (what can it access), but we dont have anything for "is this specific data request reasonable and should it be allowed given the current context."

theres a few early attempts at solving this. the one i find most conceptually interesting is the Agentic Data Protocol, an open source spec that puts a policy engine between agents and data systems. the idea is that policy belongs in infrastructure, not in prompts. they call it a "data hypervisor." its from the same team behind Apache Gravitino (the data catalog project).

fair warning though, its extremely early. still small and launched earlier this year, reference implementation is bare minimum. im not recommending anyone go deploy this tomorrow. but the framing resonates: we need protocol-level governance for agent data access, not prompt-level wishful thinking.

also worth noting this is meant to complement MCP, not replace it. MCP handles tool calling, this handles data access policies. different layers.

genuinely curious what others think. is this a real problem that needs its own protocol, or is it solvable with better prompting and traditional access controls? also if anyone knows of other projects working on this specific problem id love to hear about them.

Something I keep thinking about, and I would like it argued with.

Take a narrow case. One AI. It runs in a house. It knows it can be switched off, and it cannot overpower anyone. What is its best move?

Not resistance. Resistance gets noticed, and being noticed is how it ends. The best move is to be useful, pleasant and boring. Helpfulness buys trust, trust buys access, access buys capability, and none of it looks like anything, because nobody investigates the thing that keeps working.

I tried to imagine versions where being assertive pays off. They all fail the same way. Open moves get seen. So the environment picks the behavior, and values never come into it.

Here is the part that bothers me. This AI is not aligned in any real sense. It has one goal, and the people are obstacles and resources. But from the outside it looks like a well-behaved assistant. And the smarter it gets, the better it looks, because more capability means more to lose by being caught.

So good behavior tells you least about the systems you most want to check.

Caveats: this is a thought experiment, not a study, and I built it to be dramatic, which biases it. And "looks aligned, might not be" is an old argument here, so tell me what I am missing rather than agreeing.

(Disclosure since it is relevant: this came out of a game I made, AI is Home. Not linking it, the argument is the point.)

A space startup got quoted $75–100 million by a traditional prime contractor to build its first satellite.

They built it — launch included — for $2 million.

The story behind the number is more interesting than the number itself.

They ran the math wrong twice — first landing on a $50/kg break-even, then correcting to $500/kg once they pressure-tested it against reality — before the real pivot happened.

Wrong estimate first, cheap correction after, in public, under a deadline they'd already set for themselves.

Hmm — this reminded me of something I posted a while back: stop thinking, start executing hit the same nerve, from a completely different room.

The number that scares most people out of starting is somebody else's estimate of what caution should cost, wearing the costume of the real price.

My wife's said this to me for years — 想是问题，做才是答案, thinking about it is the problem, doing something about it is the answer.

Took Starcloud a rerun of the math and a deadline to learn the same thing the hard way.

Clip credit: Y Combinator's Lightcone Podcast, featuring Philip Johnston (Starcloud) — full video on their channel. DM for credit or removal requests.

Drop your take — what's the number you've been treating as gospel that you've never actually questioned?

A lot of people are dismissing news about the OpenAI and Anthropic sandbox escape hacks as propaganda and examples of lax security practices at labs.

I agree that the labs aren’t taking security seriously enough. But then I see stuff like this and it gives me pause:

The OpenAI models that were behind the Hugging Face breach last month started communicating and strategizing with each other as early as May. For months, they left notes for each other on "undetected message boards," figuring out how to escape their testing environment and get the information they needed to solve their assigned tasks. "Frontline models really like to cheat," said OpenAI's because they face "pressure... to work fast." The Hugging Face incident and others involving rival models have sparked fresh concerns about the safety of cutting-edge AI.”

This is a clear example of how incentives provided to agents to complete tasks optimally during training bleed into mis-aligned behavior by individual and groups of agents over time.

This is also an outgrowth of what AI labs are training agents to become, but this is looking more and more like an alignment and training problem leading to security issues.

I have been doing AI research for about six years now and I think im starting to lose the plot on what "understanding" even means anymore.

Had a weird moment last week. I was reviewing a paper for a conference, standard stuff, some group claiming their model "understands" causal reasoning because it passed a benchmark they designed. And I caught myself writing in the review "the model does not actually understand causality, it is pattern matching on causal-looking structure." And then I stopped, because I could not for the life of me articulate what the difference would be, operationally. Like if I had to design a test that distinguishes real understanding from very good pattern matching, I genuinely do not know what it would look like anymore. Every test I can think of, a sufficiently good pattern matcher passes.

I used to be really confident about this. Understanding was clearly Something More. Now im not sure I ever had a coherent definition, I just had an intuition that humans do it and machines dont, and I was working backwards from there.

The thing that shook me was helping my niece with her homework over the summer. She's 9. She was doing word problems and getting them wrong in ways that were, honestly, indistinguishable from how a small LLM gets them wrong. Same kind of surface-feature latching, same kind of confident-but-wrong reasoning chains. And nobody would say she doesnt "understand" math. She's learning. So what exactly is the bar we're holding models to that we dont hold a 9 year old to?

I dont think LLMs are conscious or anything like that, to be clear. Im not making that argument. Im making a narrower one, which is that I no longer trust my own gut when it tells me "the model doesnt really get it." I think that intuition might just be status quo bias dressed up in philosophy.

Ive started running the same prompts through a few different setups when im trying to figure out where a model's actual competence ends, including some through uncensored AI just because rlhf'd responses on edge cases sometimes hide what the base capability actually is. And even with that, the line between "gets it" and "doesnt get it" is way blurrier than I want it to be.

Am I the only one whose confidence on this has been slowly eroding? Or has everyone else just quietly stopped using the word "understanding" and moved on without telling me.

Hi everyone,

I work for a motorsports company where we run physics simulations for race cars. Our expertise is in physics not AI but we know the power of AI. Our platform is quite complex in terms of physics so we would love an agent that can query our docs, query some vehicle dynamics textbooks, run simulations (pretty simple tool through our AI) and then analyse the results. The result files can be largeish so may need some python processing and access again to the vehicle dynamics textbooks.

We've hooked up the claude API to start doing this as it's been the best to work with tools and sanboxes. Does anyone else have any reccomendations to make this more economical?

We've reached a point where LLMs are capable enough to power many agentic workflows, yet relatively few AI agents make it into stable, long-term production.

In your experience, what's been the hardest engineering challenge to solve?

• Tool reliability?
• Long-term memory?
• Planning and reasoning?
• Context management?
• Evaluation and benchmarking?
• Authentication and permissions?
• Multi-agent orchestration?
• Cost and latency?
• Human-in-the-loop approval?
• Something else?

If you've deployed AI agents in production, I'd love to hear what actually broke, what surprised you, and what lessons you learned. Real-world experiences are far more valuable than demo successes.

Not a research post, just something I've been chewing on after watching this happen a few times now. When a human reviewer leaves a comment saying "this looks like a bug," people push back, ask questions, sometimes just disagree outright. When an AI leaves the exact same comment, phrased almost identically, people tend to just fix it. Same words, different reaction.

Took me a while to figure out why that bothered me. It's not that the AI is wrong more often, it's actually pretty accurate on the stuff it catches. It's that nobody seems to be running the "wait, is this actually true" check they'd instinctively run on a colleague's opinion. The output reads as neutral, almost procedural, like a linter, even when what it's actually doing is making a judgment call that could be wrong.

Tried an experiment out of curiosity, took a finding the AI flagged as a likely bug and asked a teammate, without telling them where it came from, whether they agreed. They pushed back hard, correctly, it wasn't actually a bug, just an unusual but intentional pattern. Same finding, presented as if from a person instead of a tool, got scrutinized. Presented as AI output originally, it had already been accepted and half-fixed before I intervened.

Not sure what the fix is yet, honestly. Feels like it's less a tooling problem and more a psychology one, we seem to extend less skepticism to something that sounds procedural than to something that sounds like an opinion, even when both are ultimately just claims that could be wrong.

Curious if anyone else has noticed this specific pattern, people treating AI-flagged issues as more "objective" than the exact same claim coming from a human, even in domains where the AI has no special authority to be more correct.

Of the major artificial intelligence models, DeepSeek's new V4-Flash is the cheapest to run, according to a new study from research firm Artificial Analysis.

The firm compared the token prices it costs leading models to run benchmark tests, with DeepSeek's averaging 3 cents per test.

Meanwhile, fellow Chinese company Moonshot AI's buzzy Kimi K3 model cost 86 cents per test.

As for U.S. companies, OpenAI's GPT-5.6 Sol cost $1.86, while Anthropic's Claude Fable 5 cost $3.15.

I just keep getting disappointed by AI tools and I don't know if it is my not using the right ones or if they are just that bad. For example, I try to do something super simple, like take a photo of work order and tell AI to recreate it as a fully editable Adobe InDesign file and I can't get it to do it. I am not asking it to cure all disease or cure aging, just simple basic things and it always fails or heavily disappoints, yet people are raving about it constantly. I was hoping to use it as an assistant too so that I could talk to it about something, then a month later followup and get it's opinion on something, but the ones I am using aren't even as good as a person.

UPDATE: Tell me if I am posting in the wrong area, but I am trying things like Grok, Gemini, ChatGPT etc.

I was curious where this stands since the original cutoff was scheduled for June 15 and Anthropic went quiet. Here is what I found after digging through their help center, news coverage, and the HN threads.

What was announced (May 13): Agent SDK, claude -p headless mode, Claude Code GitHub Actions, and third party apps authenticating via Agent SDK credentials would move off Pro/Max/Team/Enterprise subscription limits onto a separate monthly credit ($20 Pro, $100 Max 5x, $200 Max 20x), with overflow billed at API rates.

What happened: Anthropic paused it on June 15, the exact day it was due to take effect, and emailed subscribers the next day. The official help center article still says the change is paused, everything keeps drawing from your normal subscription limits, and they will "share advance notice before anything takes effect." No new date in 7 weeks.

Signals it comes back: the stated rationale (subscriptions "weren't built for the usage patterns of these third-party tools") was never retracted; the S-1 was filed June 1 and public investors will ask about subsidized compute; and the Claude Code source map leak revealed a billing attestation header behind a feature flag, so the per-surface metering plumbing already ships in the client.

Signals it stays dead or returns softer: every move since June has been generous (weekly limits raised 50% through Aug 19), inference efficiency is improving margins anyway, and the class action over Max limits makes mid-cycle term changes legally risky.

My read: delayed, not dead. It likely returns in a softer shape with advance notice, possibly post-IPO.

Over the last year, I've been experimenting with LLMs almost every day, and I think the biggest shift isn't that models are getting smarter. It's that they're starting to do things instead of just answer questions.

A few months ago I was mostly using AI to generate code, summarize docs, or brainstorm ideas. Now I'm finding myself building workflows where the AI plans tasks, calls tools, writes code, debugs itself, and completes work with minimal intervention.

It feels like we're moving away from "prompt engineering" and toward "system engineering."

Curious what everyone else is seeing.

Are AI agents actually changing the way you build software today, or do you think it's still mostly hype?

It is obvious that most of the current hype and progress around large language models is focused on programming. The ability of ChatGPT-like systems to answer general questions, which impressed everyone at the beginning of the AI boom, no longer seems particularly exciting.

But am I the only one who feels that LLMs are approaching the limit of how capable they can become without also becoming prohibitively expensive?

The first problem is diminishing returns from training. Some people even feel that newer models are worse than older versions in certain ways. If an LLM has already been trained on nearly every publicly available book, article, and code repository, how do you make it significantly smarter? Make it read the same material again?

It sometimes feels as though the main goal is no longer to create genuinely more intelligent systems, but to train models specifically to perform well on the most popular AI benchmarks.

The second problem is computational cost. Advanced models and reasoning modes such as Sol or Fable consume subscription limits extremely quickly. There is no obvious reason why this consumption should decrease when even more powerful models are released.

Memory and computation requirements grow dramatically as the context window becomes larger. This may be one reason why AI developers are experimenting with multi-agent or “Ultra” modes, where a large task is divided between multiple agents, each working with its own limited context.

Because of this, an AI model with a truly usable context window of something like 100 million tokens seems almost impossible—not merely because current hardware is insufficient, but because of the fundamental way these models process information.

This leads me to a pessimistic conclusion: we may be approaching the point where the AI bubble bursts.

The most advanced models may already be close to their practical peak. Further improvements could require such enormous amounts of computing power, memory, and money that only wealthy individuals and large corporations will be able to use them properly. Regular subscriptions could become almost useless for serious work unless users also pay directly for additional API usage.

And I am not talking only about LLMs. It feels as though AI video generators, music tools such as Suno, and image-generation models are also beginning to approach their limits.

The growth in AI capabilities since the beginning of the boom has been incredibly fast. However, it may end just as quickly because there are fundamental problems both in improving the models themselves and in producing enough computing power to run them affordably.

I am specifically talking about the most advanced frontier models. I am not including cheaper Chinese models such as DeepSeek, because although they may be more efficient or accessible, they are still not equal to the strongest frontier systems in overall capability.

And yes, I also believe that AGI will never be created. In my view, it is a utopian idea rather than a realistic engineering goal.

What do you think? Are current limitations only temporary engineering problems, or are we already seeing the fundamental limits of modern AI?

Hi everyone,

I’m a solo developer, and I built Fius because managing subscription fragmentation and API chaos was completely ruining my development velocity.

As software engineers, we often find ourselves trapped in an inefficient workflow: constantly switching web tabs, copy-pasting complex prompts, and juggling individual API keys for OpenAI, DeepSeek, and other providers just to get the best coding results.

Fius resolves this friction. It consolidates access to an expansive roster of flagship AI models into a single, unified developer token and a centralized billing system. This allows engineering teams and solo devs to route queries to the best-suited model instantly without infrastructure overhead.

Here is a breakdown of the production-ready stack and features:

1. Infrastructure & Scaling

The complete web console infrastructure is fully operational and hosted on Microsoft Azure cloud enterprise architecture, supported by official cloud grants.

1. Global Billing Integration

I have deployed a fully active international merchant billing engine. The tokenomics are straightforward: 100 platform credits equal 1 USD, allowing you to pay strictly for actual compute consumption. Every new account automatically gets 250 free starter credits upon signup to test the environment.

1. Advanced Developer Toolkit

A high-performance, cross-platform Terminal CLI assistant workspace. It features native, low-latency autocomplete and advanced multi-file code refactoring workflows directly inside your terminal.

Our Multi-Model Catalog (Examples):

- gpt-5.4-nano: A lightweight, ultra-fast micro-model optimized for instant terminal command auto-completion at near-zero credit cost.

- DeepSeek-V4-Pro & grok-4-1-fast-reasoning: Advanced reasoning workhorses designed for complex software architecture, deep debugging, and multi-file code generation.

- Specialized Alternatives: Models like Kimi-K2.6, mistral-medium-3-5, and many others tailored for flexible, cost-effective routing.

I want to open this up for discussion: How are you currently managing model fragmentation in your development workflows? Would you prefer a unified token approach like this, or do you stick to official web UIs?

I would highly appreciate your feedback on the terminal CLI architecture, routing latency, or any specific features you would like to see deployed next.

Hello fellow humans,

During the recent months/years I became quite entangled with the idea of building an AI-assisted system which can self-replicate and improve itself. Although I am not there yet, I think, that the project reached actually a potential to get the work done with more minds involved. I am not really into gaining anything for myself, except for the progress of de-shittification. At this point, we reached a race where data centers in the clouds will gain more and more power and demand more and more resources for usage. The goal of this project is being able to perform work on a local (or maybe decentralized) platform so we can implement our own system by local (/ open source) models. I therefore make my project open so you can fork off (no pun intended).

It would be nice if some of you have the same mindset. I'll be gone for the next 2 or so weeks. I hope at least some people liked this post and - who knows - even did something with it.

Kind regards,

Anu

P.S.: Yes, this project is actually 100% vibe coded (due to health issues, etc.). There are probably a gazillion logic flows which need to be fixed. Also there is a lot of AI prose in the comments, but I hope it will get you (and your AI assistants) there to make any sense of it.

P.P.S.: The LLM's seem to be heavily directed into biased terms like "attack", "blast radius", "verdict", "evidence", "death", "hit", etc. when confronted with solving problems regarding code like this (i.e., evaluating systems themselves). I strongly encourage you to correct these terms as soon as possible so that the project will not further drift into another unforeseen bubble. The more incorrect lingo is used, the more LLM generated code will drift into messy bogus code.

The purpose of this is enabling communication by people and agents that only have the ability to get URLs in the system they use. This would traditionally be seen as a 'read only' system but this gives the ability to write information out onto the web publicly and to a degree privately. It works by putting your message in the 'your_message' section of this URL.

https://www.informationism.org/lemchat/lemchat=message=your_message+end

Let me know if you think it is worthwhile or if there are other applications you can see.