speakingmoistly
speakingmoistly on Hacker News
LLMs hardly to clean room anything when their training corpus is most likely "all the code you can get online regardless of license".
> Software value in free fall.
I'd challenge that. A lot of what's being produced is slop, that's the low value part. Good software still has plenty of value because it requires a lot of effort to create.
"The things they'll be able to do for us are going to be awesome. People will want them, and they are really useful."
Feels like someone has AI stock they need to see go up.
That's just globally true. For all I know, bots may outnumber both of those groups as well.
> And if so, could that explain some of the negativity towards speculative and crazy ideas?
Do you have specific examples in mind? I can't conjure links and am just operating from memory, but I recall reading about lot of ideas that are less speculative/crazy and more nonsense that feels out of touch with what actual humans would want or need.
That's only true if consumers are willing to give up the ecosystem and move elsewhere if they don't. The same way that users have (somewhat) accepted Windows's own sins as the cost of doing business, I'd expect the same here.
I 100% believe that it's a formula that may work up to a certain scale as long as the whole operation toes the happy path line, but I wonder what happens when shit hits the fan in any meaningful way. Businesses adopting products is largely about trust, and having a vendor whose bus factor is essentially infinite feels risky unless it's a mundane service that doesn't process any data, that you don't mind being unreliable and that you are comfortable with it becoming unpredictable.
Not only that, but outsourcing so much to LLM providers feels like a recipe for degraded service (of which there is a lot, Anthropic's status page lists about an event every day or two and save for Claude Government, Anthropic is barely hanging on to two nines uptime) to take out the entire business.
Now, as to the two examples that the WSJ cites. Ben Broca's thing is Polsia, which resells agent services so that other entrepreneurs can do their AI-powered solo thing. It came up in news a while back because of how it exposes what the platform is doing to the public in real-time. Every time I look at it, it feels fishy -- right now, it lists ~3000 companies launched today, which may be true but doesn't quite sound right. I'm skeptical of how viable it is as a business, but I'm welcoming being proven wrong there. Until then, it has a strong "buy my sales webinar" vibe.
Troy Johnston's product is also in the same vein, selling consultant services to help entrepreneurs to leverage AI. Again, having an AI-powered business selling the AI-powered business dream isn't exactly that far from the webinar energy of the last one.
I couldn't find much about Samir Ahmad's venture, but seeing how it folded within a few months, it doesn't look like it really took off the ground.
Finally, Claire Vo's product looks like it might be ChatPRD, which looks largely like a wrapper around frontier models writing product documentation - this is what I would expect the whole one-person-startup to be like. Worth noting that they have since hired staff.
In the end, it seems like it's less "AI runs my million-dollar solo venture" and more "AI helps supporting the idea in the early days, but can only go so far and then you hire staff". That last bit is less flashy than WSJ's title work though.
If the former, the answer to your question is always "yes": if there's something to learn and you're interested in that problem space, it's always worthwhile.
If you're looking at the latter, you're essentially building a product. The next question should then be "is there part of what's out there that you're confident you can do differently/better?"
The real mistake was CEOs feeling like they need to be visible on social media and opine on things in the public sphere.
> Some start-ups may share digital data needed to train A.I. models.
Your data being used by the companies you're dealing with directly is one thing, but it being open for untold loose partners you have no connection too feels like it should be a bridge too far.
I'd be curious to hear about whether this has an edge when it comes to performance in any way, or whether it solves for a problem that other formats are missing the mark on.
By the time you turn around on bad data, much more damage can be done.
Whoever is operating it (as in, you or the entity providing you the service if you're using someone else's thing). If I operate a coffee machine accessible to others and it injures someone, I'm on the hook; the same logic applies to an agent. At the end of the day, LLMs are tools, and whoever is overseeing it is vouching for it (and paying the price if it misbehaves).
> Personal accounts are risky and manual LLCs don't really scale?
When it comes to personal accounts, I assume you're talking about use cases like "I use an agent in my personal capacity to do things and it made a mistake". This sounds like you're shouldering the risk and accept the potential consequences. That's just a case of "I did something risky, and found out".
As for the manual LLC bit, I'm assuming you are thinking about an agent as part of a business. In that case, whoever is operating the business is on the hook.
The idea that agents should be legal entities just sounds like an attempt at shifting blame away for doing risky things knowingly.
> The team that builds and runs Echon works out of Canada; operational data is processed both in Canada (where the team accesses it) and in the United States (where our infrastructure is hosted).
That aside, the website doesn't really emphasize the privacy angle, so I'm curious about what makes this "privacy-first".
> [...] Only roles credited in the film’s legal billing and demonstrably performed by humans with their consent will be considered eligible. [...]
[edit] Now, the above is in the 2026 rules, but before this year, animals were still not nominated or awarded Oscars despite the rules not explicitly stating that animals are not eligible. Looking around, there's a lot of online discussion about this across the years. There are/were other award ceremonies for animal performers though, for example the PATSYs [2]. [/edit]
Now, why not an AI actor? For the same reason LLMs are not colleagues in the workplace. The anthropomorphization of AI has already gone far enough (case and point, the entirety of /r/myboyfriendisai on Reddit, or any discussion of personal and intimate relationships with AI), let's not legitimize it further. At best, it's special effects, not a cast member.
[1] See Rule 6, first section - https://www.oscars.org/sites/oscars/files/2026-05/99th_oscar...
That being said, I would assume that like most DIY things, part of the purpose of it is the creative journey that leads you to assembling yours. You learn things along the way and have an interesting artifact to show for it once you're done.
I'm pretty sure all of the LLM subscription businesses operate at a loss when it comes to fixed-price subscription [1]. The price needs to be low enough for regular consumers to consider "sane" (most people wouldn't pay more than 20$ monthly, it fits in well with the rest of the subscriptions people have been trained to tolerate), but usage quotas probably can't (citation needed, but it feels like a reasonable take) be constrained to an amount that would allow for real profit while remaining high enough to be useful (or at least non-obstructive).
LLMs remain a tool, they amplify what's there (good and bad), but the real predictor of success is the humans using it. Think of it as baking: adding LLMs to your toolbox is a lot like getting moving from mixing dough by hand to having a stand mixer. You'll be able to mix a lot more dough with the mixer, maybe even have your chefs and apprentices do something else than mixing dough for hours. Even if you deck out your kitchen with the latest machines though, your bread will only be as good as the knowhow of your people and the recipes you use. The same applies to software.
One of the key selling points of LLMs has been that it makes execution cheaper (in software-land, at least). This is arguable, but assuming it does, execution is still not the bottleneck as they'd like you to think. In my experience, deciding what to build, collecting insights on what's out there and how the rubber meets the road with users, and building organizational alignment on the path ahead remains the hardest, most tedious part of the process. LLMs may help with some of it, but in the end, this is still people-driven, and regardless of how many tokens or fancy models you have at your disposal, that's what will decide if your solution to a problem stands out against your competition.
The equation may also be a bit different if there's disclosure. If an author is being upfront about AI assistance that would materially affect the text, I might read further (provided that it's not just "AI wrote this and I barely proof-read it).
Ultimately, I'd be less likely to spend time and effort consuming something I have the feeling wasn't really the product of someone else's time and effort.