3,312 karma · joined February 16, 2010
[ my public key: https://keybase.io/aeontech; my proof: https://keybase.io/aeontech/sigs/ITWRib8X6w055xVMUK6vRaEenJI_nAS8J0Flloe0Bl0 ]
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A ten year old kid left in an ambulance, neck injury, one side of the body paralyzed at the time of the event.
I really hope the kid recovers... but I don't think I'll ever forget that image.
Yes, everything in life has risks, but I don't even want to imagine how I'd feel if my kid, or my kids' friend had a life-changing injury on a trampoline at my home.
Prompt: "Can you try solving it, instead of not solving it?"
AI: "Oh, sure, here you go!"
That's not been my experience, tbh - in my view the deletionist fraction of the editors has essentially "won", if one can put it in those terms. I _think_ there is a (maybe small) group that have decided it is their mission to guard Wikipedia against what they view as cruft or non-notable, regardless of how many years of work these articles may have accumulated. They do not need to be paid for this - they enjoy it. Destroying is always easier than creation.
I seem to recall some study showing that the vast vast majority of edits/deletes on Wikipedia are the work of just a few hundred long-standing editors (citation needed) - which to me confirmed my gut feeling that most new editors bounce off and give up on contribution in short order.
I contributed for a few years, but gave up eventually - it was exhausting to spend time collating sources, collecting information, editing, rewriting, and then having someone come along and propose discarding your work with very little investment from their side.
Stackoverflow has gone through similar calcification - it's nearly impossible to contribute now, or build reputation as a new user, as posts get closed as duplicates or not-relevant.
Looks very interesting though!
Lemma 2: you are obliged by other regulation to offer equal access to user data to third parties, so others can build equivalent functionality (DMA).
Lemma 3: malicious third parties will absolutely try to abuse the access and trick the user into sharing their data by all means possible. You will be held responsible in court of public opinion at minimum and legally at maximum if/when a malicious third party abuses said access.
This is a hard, possibly technically unsolvable problem no matter how much money you might have, because the root issue is not technical, it's the fact that you legally have to give third parties access and no way to control what they do with it - and as others have mentioned in the threads, it's exacerbated by the fact that the regulation doesn't say "this is okay and this is not", it is vague and judges things "by outcome", so you may spend all the time in the world implementing a solution you think will work, and then get hit by fines/lawsuits because the implementation is judged as not sufficient after the fact.
Haven't seen pg_plan_advice before, TIL!
My usage has gone way down since switching to streaming services, this made me search for ways to backfill the data...
For anyone using Apple Music, looks like you can request an export of your data from privacy.apple.com, then use https://github.com/nerveband/Apple-Music-Play-History-Conver... to convert it for Last.fm import (haven't tried it yet, but looks promising).
My point is, it feels safer and easier to aim to enter a new lane with the aim of "following" someone, rather than trying to rush in "ahead" of someone. But maybe it's just me.
This ensures that
a) I do not cut anyone off accidentally, and minimize the amount of stress in my immediate part of the universe
b) I will (most likely) have plenty of room behind me after I change lanes, reducing chances of anyone else running up on me
c) If there's noticeable traffic, the time I spend signaling and waiting for the person to move slightly ahead of me gives plenty of warning to the people _behind_ them that I'm about to enter the lane.
Ultimately, yes, of course in principle you're right, when I change lanes, I enter the lane in front of someone.... but I _can_ control whether I enter as far as possible ahead of them.
I probably wouldn't write a training system in Ruby (not because it's not doable, just because it's not a good use of time to rewrite stuff that is already available in python ecosystem)... but hooking up a Ruby system up to LLM's for interaction is eminently doable with very little effort.
I am assuming your situation had some specific constraints that made it harder, but it would be nice to understand what they were - right now your comment describes a more complicated solution and I am curious why you needed it.
Now whether the on-paper prices for medical care in this country actually have any relationship to objective reality is an entirely separate question of course. In general coming from an outside perspective, combining healthcare and for-profit motives in a single system seems particularly likely to lead to all kinds of perverse incentives, but, it's the system that exists, and it seems unlikely to change any time soon.
As another comment here noted, the title is missing (2024) - this model was released almost a year ago, last December, so it's not surprising that that's the models they compare to.
I wonder if there's any statistics comparing deaths and injuries from drunk driving versus distracted driving over the past 20 years or so. Is it a comparable at all?
Oh man, regardless of how "bad" someone's writing is, this is terrible terrible teaching. Public shaming in front of peers, especially on something subjective like this? Some people should not be teachers. I'm sorry you had to go through that.
Or, with the subject matter being tech, most potential readers are fluent anyway?
Very cool to see a team use Jepsen for super early pre-release testing of the system.
I wonder if you wish you had waited for the runtime to be a bit more stable, or you feel this was already well worth the effort, even with some of the identified failures being in "known incomplete" areas? (I could see either side of the argument - waiting longer might give you more valuable failures, but testing early gives you a chance to catch problems before they become baked into the foundation and become more difficult to fix...)
Another tool that feels like sci-fi to me any time I hear a mention of it, is Antithesis [1] - written by the people who built FoundationDB. Could be another interesting integration to investigate in the future to help bulletproof the language runtime?