The CD joke aside, there's something weird abouta large, recognizable entity using such a nondescript MS form and collecting names and addresses, only to send something to a very small segment of them. Feels like a data harvesting operation.
At the very least, I would imagine you'd read and think about the error, parse through and clean it up for unneeded bits if the whole thing isn't relevant and write around it to provide context on what happened / possible things to correlate with it in case you have hypotheses to work out.
I'm also not confident this is something that will stick (and survive the acquisition), which further weakens the idea that companies would set up expensive migration projects to it.
Is the goal to build something and learn something from the experience, or try and build something with adoption in mind?
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?"
Not too long ago, a recruiter reached out for a confidential client that turned out to be ClickUp with a posting that felt off (600-900k + stock and bonus compensation), I was left wondering what kind of insanity is rolled into the job for this amount. Now I know.
I'm a bit confused about the offering here, it feels like Amp is positioning itself as a reseller of sorts. Couldn't the startups just purchase compute directly from AMZ/GCP/Azure? Through direct or indirect ownership, I'd bet that the datacenters being dealt with would be under those anyway.
> 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.
How does this compare to YAML and TOML? It feels that both of the above would be in the same bucket of "fewer tokens than JSON/XML and generally easier on the eye", with the benefit of having a pretty rich ecosystem already.
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.
> When an agent spends money or creates liability, who's responsible?
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.
A hot take: it's hard to take a privacy-first claim without a large grain of salt when data at rest is hosted and processed in the U.S., an increasingly-rogue actor with no meaningful privacy laws.
> 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".
This is an interesting rabbit hole, but animal actors are not eligible for Oscar nominations [1]:
> [...] 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.
Given that it's a pretty niche thing, a link or post context wouldn't hurt.
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 have yet to read into the actual announcement, but I would bet dollars to donuts that the check is really "run the plan through another LLM whose system prompt is prefixed with 'you are great at safety checks'".
> Maybe the root issue is that Anthropic is operating at a loss on their subscriptions
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).
The people selling those services have worked really hard to get everyone else to think that more AI usage inevitably leads to more success (and inversely, that too little of it is a signal that you're being left behind), but I have yet to see this actually backed up by anything else than marketing.
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.