Very farsighted, after working as a patent clerk, to lay claim on such a foundational technology. Back in the day, they must've been like, oh, so Mercury blocks the sun at the wrong time, but where's the commercial value - and now every chemical company throughout the universe is about to get a bill every time they make something more complex than hydrogen gas.
Meanwhile, Galilean relativity has long gone out of patent, and people on board planes and other vehicles just move around like they were in a stationary reference frame paying no royalties.
Back in my day, a software company of national importance would be considered a big success if acquired for 60 million... Of course, at that point it would have to be profitable, and actually own its key IP...
Not quite the same scenario, but it's already plausible to have a situation where every subagent is allowed to spawn multiple subagents, in which case we'd have literally exponential credit consumption growth...
Startup working on AI Healthcare solutions (plus some random experiments) | REMOTE(US, Pacific time zone) | Full time and project based
Full time engineering role (Remote, US person required; Pacific time zone)
Stack: C#, Python, Gemini API on Windows, lots of legacy systems/constrained enterprise environment. Health systems experience preferred. Understanding of cost effective, reliable LLM API use. Should be flexible: do PM-like work with less-technical client domain experts, solve problems end to end, willing to data cleaning and detailed work for client goals and security/compliance gruntwork, even when it's not technically elegant. Self motivated, willing to understand legacy and undocumented codebases, but also willing to take instructions.
Ideal candidate has real (pre-Claude Code) experience, but is ready to do fast iteration in post-AI world, while actually reading what the AI does. We're ok if your stack is different, if you're both technically mature and up to date on modern fast-turnaround AI-based development. Probably in the 5+ year experience range.
REMOTE anywhere, project based or initially part time: startup generalist hacker, maintain systems and websites, code one-off prototypes. Should be good enough to code without an AI in a few languages, but an effective AI harness coder. Can do a small but complete app/project end to end, including basic marketing. 2+ year exp or a new grad with a good portfolio or major project; happy to consider more experienced person. Good role for someone who's working on their own startup to pick up some side money and experience. Projects could be anything from game-like apps/novelties to voice tools to utility apps.
REMOTE anywhere, project based or initially part time: help on some more experimental mathy projects. M.S./Ph.D. in statistics, physics or related, with pragmatic programming experience. Possible projects: gaussian process modeling; designing evaluation metrics; compression; Bayesian tools for radiology imaging. May be good for a grad student part time. This is the fun stuff the founder would rather be doing himself if he had the time.
REMOTE (US person) Half-time remote admin/chief of staff type: deal with bookkeeping, compliance, communications, social media, phone calls - save us time on everything non-technical. Should be effective AI user; should have some relevant experience supporting tech business.
File systems are nice if you need to do manual or transparent script-based manipulations. Like 'oh hey, I just want to duplicate this entry and hand-modify it, and put these others in an archive.' Or use your OS's access control and network sharing easily with heterogeneous tools accessing the data from multiple machines. Or if you've got a lot of large blobs that aren't going to get modified in place.
What the world needs is a hybrid - database ACID/transaction semantics with the ability to cd/mv/cp file-like objects.
The new workflow will be "AI, I need to view this text file and add some words to it. Create an app that displays it in a scrollable window, respecting the encoding. Now move the cursor to the line below the three dashes... no, the other three dashes..."
well, sure, that uses a large number of processing cycles for each small operation. But asking a frontier LLM to evaluate a lisp expression is more or less on the same scale (interesting empirical question whether it's more or less). And, if we count operations at the brain neuron level it would take to evaluate one mentally....
And they're pretty much the only example of an embedded browser architecture actually performing tolerably and integrating well with the native environment.
That's one maxed out RAM configuration. Back in my day, we had 4k RAM, about 3500 bytes usable from BASIC, and that was enough, unless you were rich enough to have a 3k memory expansion cartridge. But really, if you need that extra 3k, you're just not writing code efficiently enough, right.
We're just not going to see any code written entirely without AI except in specialist niches, just as we don't see handwritten assembly and binaries. So the disclosure part is going to become boilerplate.
In the old era, the combination 'it works' + 'it uses a sophisticated language' + 'it integrates with a complex codebase' implied that this was an intentional effort by someone who knew what they were doing, and therefore probably safe to commit.
We can no longer make that social assumption. So then, what can we rely on to signal 'this was thoroughly supervised and reviewed and understood and tested?' That's going to be hard and subjective.
Personal reputations and track records are pedigrees and brands are going to become more important in the industry; and the meritocratic 'code talks no matter where you came from' ethos is at risk.
Yup! My point is that the 'coin flip baseline' model that's as good as chance isn't actually trivial to create, for an unbalanced and time varying underlying distribution.
The threshold isn't 50% because the distribution of human and AI written cases isn't naturally 50-50. So a coin flip will underperform always guessing the more frequent class. Where it gets interesting is if the base is unknown or variable over time or between application domains. Like, since AI written text is being generated faster than the human kind, soon guessing AI every time will be 99% accurate. That doesn't mean such a detector is useful.
I'm being hyperbolic of course, but I'm a little dismissive of the progress that happened since the days of BBS's and car based cell phones - we just got more connectivity, more capacity, more content, bigger/faster. Likewise, my attitude toward machine learning before 2023 is a smug 'heh, these computer scientists are doing undisciplined statistics at scale, how nice for them.' Then all of a sudden the machines woke up and started arguing with me, coherently, even about niche topics I have a PhD in. I can appreciate in retrospect how much of the machine learning progress ultimately went into that, but, like fusion, the magic payoff was supposed to be decades away and always remain decades away. This wasn't supposed to happen in my lifetime. 2025 progress isn't the 2023 shock, but this was the year LLM's-as-programmers (and LLM's-as-mathematicians, and...) went from 'isn't that cute, the machine is trying' to 'an expert with enough time would make better choices than the machine did,' and that makes for a different world. More so than, going from a Commodore Vic 20 with 4k of RAM and a modem to the latest Macbook.
Might be a good place for yunohost/coolify style services, especially if you have multiple separate entities - though probably tricky to do inbound mail because of IP allocation?
Meanwhile, Galilean relativity has long gone out of patent, and people on board planes and other vehicles just move around like they were in a stationary reference frame paying no royalties.