I mean, it was named for fictional spying orbs that could break people's minds based on the will of a powerful individual with access to the system. Maybe that's not them telling on themselves, but I am not surprised that some people wind up suspicious.
Not ideal, between sound from the washing itself plus whatever else is going on in the house, and I'd rather not add noise that others have to deal with. That said, it's certainly in the mix as competing with other potential solutions.
Just in case you aren't joking, they did not fire the person responsible for the bad numbers. They fired the person responsible for observing and reporting them.
I was joking, although it was a gesture at a real thing. I don't know how much they don't care about success, versus caring but being deeply confused about how to reach it, versus in some sense "caring" but prioritizing other things. But regardless, retaliation against bearers of bad news is a great way to horrifically mismanage an organization, and we seem to be seeing it repeatedly from this administration, and it is alarming.
Speaker diarization is the term you are looking for, and this is more difficult than simple transcription. I'm rather confident that someone probably has a good solution by now (if you want to pay for an API), but I haven't seen an open-source/open-weights tool for diarization/transcription. I looked a few months ago, but things move fast...
Diarization is on the roadmap; some providers support it but some don't and the adapter for that could be tricky. Whispering is not meant for meeting notes for now; for something like that or diarization I would recommend trying Hyprnote: https://hyprnote.com or interfacing with the Elevenlabs Scribe API https://elevenlabs.io/app/speech-to-text
Thanks, that, yeah. I've looked occasionally but it's been a bit. Necessary feature in a house with a 9yo. I've been thinking about taking a swing at solving my problem without solving the general problem.
I ran into a question a while ago that I couldn't find a good answer to, and while it's not exactly on topic this seems like a good place to ask it.
I was working in a detail rich context, where there were a lot of items, about which there were a lot of facts that mostly didn't change but only mostly. Getting a snapshot of these details into approximately everyone's head seemed like a job for spaced repetition, and I considered making a shared Anki deck for the company.
What wasn't clear was how to handle those updates. Just changing the deck in place feels wrong, for those who have been using it - they're remembering right, the cards have changed.
Deprecating cards that are no longer accurate but which don't have replacement information was a related question. It might be worth informing people who have been studying that card that it's wrong now, but there's no reason to surface the deprecation to a person who has never seen the card.
Is there an obvious way to use standard SRS features for this? A less obvious way? A system that provides less standard features? Is this an opportunity for a useful feature for a new or existing system? Or is this actually not an issue for some reason I've missed?
For what you describe, he ideal system would do this:
1. Identify knowledge blocks that you want people to learn. This is what would be tracked with the SRS.
2. Create cards, with a prompt which requires knowledge blocks to answer. Have the answers in this system feed back knowledge to the SRS.
3. When one of the knowledge blocks changes, take the previous knowledge familiarity and count that against the user.
So for example, at some point a card might be "Q. What effect will eating eggs have on blood cholesterol? A. Raise it." That would be broken down into two knowledge blocks: "Cholesterol content of eggs" and "Effect of dietary cholesterol on blood cholesterol".
At some point you might change that card to "Q. What effect will eating eggs have on blood cholesterol? A. None, dietary cholesterol typically doesn't affect blood cholesterol." (Or maybe we're back again on that one.)
The knowledge blocks would be the same, but you'd have to take the existing time studied on the "Effect of dietary cholesterol on blood cholesterol" and mark it against recall rather than towards recall. Someone who'd never studied it would be expected to learn it at a certain pace; but someone who'd studied the old value would be expected to have a harder time -- to have to unlearn the old value.
I think you could probably hack the inputs to the existing FSRS algorithm to simulate that effect -- either by raising the difficulty, or by adding negative views or inputs. But ideally you'd take a trace of people whose knowledge blocks had changed, and account for unlearning specifically.
You could have a company-provided Anki account for each user where you add and remove cards just for that user. (I thought you might even be able to use your own server, but that doesn't seem to be an option for the iOS app: https://ankicommunity.github.io/Tutorials/anki_custom_sync_s... )
Then placing a "this has changed" notification card at the front of the new queue only for people who learned the old information is as simple as checking the corresponding card's review status in the database.
This specific problem gave us lots of headache while building https://rember.com
We don't have a good solution yet. My hope is that something like content-aware memory models solve the problem at a lower level, so we don't have to worry about it at the product level.
I stand corrected, chat bot agrees, it's most like Ohio or Illinois in population and GDP.
> Ohio has long been a national leader in EHR adoption, with nearly 5,000 primary care physicians signed up through the Ohio Health Information Partnership—more than any other state as of around 2011.[1] Cincinnati-based HealthBridge operates one of the largest and most robust regional Health Information Exchanges (HIEs) in the U.S., servicing over 30 hospitals and 7,500 physicians across multiple states.[1]
> In Ohio, a qualitative 2022 study surveyed provider and leadership perspectives on interoperability, finding high adoption rates: 96% of Medicaid‑PI‑eligible providers and hospitals had adopted EHR systems; non‑eligible providers reported adoption at 72%.[2] Epic Systems dominates the state as the top EHR vendor—used by 37% of Medicaid‑PI recipients and over 56% of other providers; smaller practices more often use NextGen, eClinicalWorks, etc.[2]
> The 2021 Illinois Health IT Survey, based on 175 respondents representing ~3,800 providers, shows 100% EHR adoption among respondents—up from 61% in 2011.[3] Participation in an HIE rose from 32% in 2016 to 51% in 2021.[3]
> For Illinois, key barriers reported: lack of provider Direct message addresses, reluctance of referring providers to accept messages (58%), and vendor cost constraints (46%).[3] Top reported improvements: decreased medication errors (64%), improved throughput (60%), and better reporting and referrals (60% and 57%).[3] The most difficult challenge: meeting program objectives (37%), followed by implementation cost and time (22%).[3]
Overall chat bot indicates Poland has unique patient IDs so no record duplication compared to poor US implementations, high interop within P1 compared to poor interop between US vendors, and good patient data access compared to poor implementation by US vendors. Chat bot gave little about burnout but mentioned Polish and US AI developments under way. I would assume there's poor interop between Poland and other EU states, likely much worse than the US IMHO. Not really any mention of other topics like clinician workflow, burnout, and productivity re Poland.
> It isn't actually possible for fixing a bug to break a workaround.
That's not true. For instance, if there's a bug in formatting, that might be worked around by handing the unintended formatting. But now you're (maybe) not handling the intended formatting, and a fix would break you.
Well, no, it indicates that it is sometimes impossible at a fundamental level. That doesn't speak to whether it's a good idea some of the times when it is possible.
