it’s only as good as it’s training data and how it interprets it. thus you can never have an LLM in any kind of leadership or major decision making role simple because of GIGO - Garbage In, Garbage Out.
they’re painfully easy to manipulate or simply by feeding it bad data. In this case it fucked up it’s time keeping thing which shouldn’t surprise anyone who has done any sort of coding with an LLM and then an idiot human being prompted it to make a decision based on garbage data. It’s like asking a hammer why it failed to manage the construction crew building a house. doesn’t work like that and should never work like that.
In this case it fucked up it’s time keeping thing which shouldn’t surprise anyone who has done any sort of coding with an LLM
This time and earlier last year, yeah. Try again with the newer Anthropic tools and you’ll find them to be rather thorough and correct with their calendar and clock math, handling of edge cases like DST shifts, leap years, etc.
Yeah, I “vibe coded” a timesheet app in about $15 of Claude subscription credits. Actually did it twice: $10 for the Python version, then migrated that to Go for $5 - it does run a lot faster in Go while simultaneously consuming less power / resources. It makes a couple of nicely formatted timesheets, serves multiple clients with multiple service providers, tracks everybodys’ shifts, providers can log in from their phone and accept/drop shifts, schedulers can create new open shifts for providers to consider, etc. etc. - been using it for about a month now, so far the math is flawless - as compared to the human+calculator math that was being used before which seemed to have at least 1 error per 10 shifts computed.
Andon Labs, which tests the limits of AI agents, gave Luna a $100,000 budget, internet access, and a corporate credit card, along with instructions to open a store and turn a profit.
Luna, built using Anthropic’s Claude models, selected merchandise, hired contractors, posted jobs on Indeed, interviewed applicants, and hired employees for Andon Market, which sells books, candles, prints, games, and branded merchandise. Andon Labs provided support for more difficult tasks, such as permitting, but said the lab tried to be as hands-off as possible.
The store has generated sales but is not profitable.
That’s pretty impressive. It constructed an entire business using only some string, a squirrel, and a business.
Meh.
Every AI story turns out to be less of a sensation when scrutinized. It’s still a machine, doing what it’s prompted to do.
Its almost like we were told very early on, that a machine cannot be held accountable and therefore a machine cannot be in management
it’s only as good as it’s training data and how it interprets it. thus you can never have an LLM in any kind of leadership or major decision making role simple because of GIGO - Garbage In, Garbage Out.
they’re painfully easy to manipulate or simply by feeding it bad data. In this case it fucked up it’s time keeping thing which shouldn’t surprise anyone who has done any sort of coding with an LLM and then an idiot human being prompted it to make a decision based on garbage data. It’s like asking a hammer why it failed to manage the construction crew building a house. doesn’t work like that and should never work like that.
This time and earlier last year, yeah. Try again with the newer Anthropic tools and you’ll find them to be rather thorough and correct with their calendar and clock math, handling of edge cases like DST shifts, leap years, etc.
Which is pretty impressive for a text generator, but if I need accuracy I’m still going to use a mathematical calculator.
Yeah, I “vibe coded” a timesheet app in about $15 of Claude subscription credits. Actually did it twice: $10 for the Python version, then migrated that to Go for $5 - it does run a lot faster in Go while simultaneously consuming less power / resources. It makes a couple of nicely formatted timesheets, serves multiple clients with multiple service providers, tracks everybodys’ shifts, providers can log in from their phone and accept/drop shifts, schedulers can create new open shifts for providers to consider, etc. etc. - been using it for about a month now, so far the math is flawless - as compared to the human+calculator math that was being used before which seemed to have at least 1 error per 10 shifts computed.
That’s pretty impressive. It constructed an entire business using only some string, a squirrel, and a business.
I’m not a monkey!