Having a couple of binaries in the git tree would be a kind act: I often do that with (much smaller!) project of mine to make it easy of people to try and use them. Of course, to create MRs, you'll still need to build and debug it yourself.

Let's make programming more fun by making it harder!

Yup, same as the feedback loops in "cold readings"

Charlie Stross(@cstross@wandering.shop) wrote, in Mastadon:
The LLMentalist effect: Large Language Models replicate the mechanisms used by (fake) psychics to gull their victims: https://softwarecrisis.dev/let...

The title of the paper is "The LLMentalist Effect: how chat-based Large Language Models replicate the mechanisms of a psychic’s con"

That's been done for years, just not by Facebook. It's a classic problem in machine learning, and they've either finally noticed ... or took a revenus hit! Not making money is an excellent motivator (:-))

Interests, like "I want a trailer hitch for my Subaru".

All that other stuff? Wanted for someone else.
It's just piggybacking the blame onto advertisers, because people don't like them anyway

• - they own the agent for the advertiser,
• - they own the agent for the publisher,
• - they own the auction house, and
• - they don't provide an audit trail.

I used to work in advertising, and I saw Google as the personification of "moral hazard" (which see). Other things? Way nicer.

If you scan a thousand British faces and compare them to a thousand criminals, you will do 1,000,000 comparisons. (that's the birthday paradox part).
If your error rate is 0.8%, you'll get roughly 8,000 false positives and negatives.
That's bad enough if they are all false positives: people get arrested, then released.
It's way worse if they are all false negatives: 8,000 criminals get ignored by the police dragnet.

That was Britain: false positives are life-threatening in countries where the police carry guns.
0.8% is a good error rate. 34% wrong is typical in matching black women. See
https://www.aclu-mn.org/en/news/biased-technology-automated-discrimination-facial-recognition#:~:text=Studies%20show%20that%20facial%20recognition%20technology%20is%20biased.,published%20by%20MIT%20Media%20Lab.

At a certain company long long ago, managers had a mainframe-based planning app that looked like a sort of spreadsheet.

The company did a study to see how much it improved the manager's team's productivity...

Oops! Use of the tools was correlated with declining productivity.

Alas, if a 16-year-old is doing the buying over the net, Bob's out of luck. Bob can't even tell if the customer is a dog (:-))
The parent who buys their kid a phone is a really good source of the kid's age. It's not leak-proof, but most other approaches leak like sieves.

Once upon a time, lint would critique your work, at the expense of often being wrong. I tried using LLMs for that, and they work only a little less well than lint.
I was insulted, though, when It misquoted my sentence and then said I had a grammar error in the misquoted part (:-))

Steven Rostedt wrote -
"I played a little with [Rust] in user space, and I just absolutely hate the cargo concept... I hate having to pull down other code that I do not trust. At least with shared libraries, I can trust a third party to have done the build and all that..."

The various crate-like things are a fad. The arguably correct way of using shared libraries was reinvented independently by the Gnu libc team and by Solaris, from a first use in Multics. You remember, Unix's papa and Linux's grandpa?

Give it a few years, the hype bubble for importing static libraries will burst, and shared libraries with updaters and downdaters will be re-re-invented.

From the Canadian Government page cited below:

A person given a "quit or return to the office" has been fired, and can sue the pants off the employer. The lawyer involved may well offer a good price on a suit to everyone the employer fired, thus increasing the risk to the employer.

See https://www.canada.ca/en/emplo..., or google for "lawyer constructive dismissal" if you're not in Canada

The company is testing humans for their ability to do something they are inherently bad at.
Filtering programs, such as the one at spamcop.net, do it well:
- I haven't had a false positive for about three years.
- I get a false negative about once a month.

Whenever I get an email at a customer's, I run it through the spamcop filter. That reliably identifies the phishing-test emails,

I prefer to report those on the equivalent of the IT slack channel, so others aren't caught out by them (;-))

That's a lot like character device drivers in CP/M, which would fill/clear their queues before returning. The queues were short.

Need I say more?