Hackers' Pub

Syntax	Description	Examples
`"` keyword `"`	Finds the string within quotes, including spaces. Case-insensitive. (Escape quotes inside with `\"`)	`"Hackers' Pub"`
`from:` handle	Finds content written by the specified user.	`from:hongminhee` `from:hongminhee@hollo.social`
`lang:` ISO 639-1	Finds content written in the specified language.	`lang:en`
`#` tag	Finds content with the specified tag. Case-insensitive.	`#HackersPub`
condition condition	Finds content that satisfies both conditions on either side of the space (logical AND).	`"Hackers' Pub" lang:en`
condition `OR` condition	Finds content that satisfies at least one of the conditions on either side of the OR operator (logical OR).	`#HackersPub OR "Hackers' Pub" lang:en`
`(` condition `)`	Combines the operators within the parentheses first.	`(#HackersPub OR "Hackers' Pub" OR "Hackers Pub") lang:en`

Paul Cantrell @inthehands@hachyderm.io

2/9/2026, 4:30:32 PM

Public

LLMs have no model of correctness, only typicality. So:

“How much does it matter if it’s wrong?”

It’s astonishing how frequently both providers and users of LLM-based services fail to ask this basic question — which I think has a fairly obvious answer in this case, one that the research bears out.

(Repliers, NB: Research that confirms the seemingly obvious is useful and important, and “I already knew that” is not information that anyone is interested in except you.)

1/ https://www.404media.co/chatbots-health-medical-advice-study/

Chatbots Make Terrible Doctors, New Study Finds

Chatbots provided incorrect, conflicting medical advice, researchers found: “Despite all the hype, AI just isn't ready to take on the role of the physician.”

www.404media.co · 404 Media

Paul Cantrell @inthehands@hachyderm.io

2/9/2026, 4:31:35 PM

Public

Despite the obviousness of the larger conclusion (“LLMs don’t give accurate medical advice”), this passage is…if not surprising, exactly, at least really really interesting.

When the researchers tested the LLMs without involving users by providing the models with the full text of each clinical scenario, the models correctly identified conditions in 94.9 percent of cases. But when talking to the participants about those same conditions, the LLMs identified relevant conditions in fewer than 34.5 percent of cases. People didn’t know what information the chatbots needed, and in some scenarios, the chatbots provided multiple diagnoses and courses of action. Knowing what questions to ask a patient and what information might be withheld or missing during an examination are nuanced skills that make great human physicians; based on this study, chatbots can’t reliably replicate that kind of care.

If you have a fediverse account, you can quote this note from your own instance. Search https://hachyderm.io/users/inthehands/statuses/116041626097492540 on your instance and quote it. (Note that quoting is not supported in Mastodon.)

Eric Carroll @EricCarroll@cosocial.ca

2/9/2026, 4:57:09 PM

Public

RE: https://hachyderm.io/@inthehands/116041626097492540

A 5.1% error rate *with a well framed question*.

Lossy compression indeed.