ericskiff
Proof of training or RAG search on data that was private would be a huge smoking gun and would change usage habits overnight.
The factors of attractiveness are by far more related to basic self care (hygine and being fit enough to care for yourself and others), kindness and the ability to share in others' joy, and passionate interest in something, and a lightness or humor in your manner.
I started balding at 18, shaved it all at 22. Its not an issue. Height, hair, etc might get immediate reaction and attention, but hardly matter for real connections.
The actual "AI deleted my database" story is really more of a "Railways' database 'backup' strategy is insane and opaque and Railway promoting AI infrastructure orchestration without guardrails is dangerous."
If removing Trunk had irrevocably deleted it from a single centralized server and also deleted any backups of it, there would have been an "SVN and the CLI destroyed our company" article back then.
As a Railway user, I appreciated that information and have changed my strategy when using them.
This and howstuffworks.com made me so hopeful for the future of the web when I was young
I love this so much, thank you for sharing!
* Slow down the motion to about .5 of what it is currently, with easing/acceleration on the speed to emulate the camera dolly and jib effects used in the film
* Add a random motion setting that allows me to run it full screen just sliding through the aisles, banking around turns, flying up and then back down the aisles.
* optionally lock the framerate to 24fps to give it a film feel
* optional shaders on the main viewport to emulate lens distortion, film grain, etc
* raytracing with reflectivity on the glass, refraction, diffusion, etc.
It's by no means a safe alcohol replacement
On further reflection, this is very interesting and I understand where the drag and drop interaction breaks down on long lists. Some additional UI affordances to communicate what's happening may make it intuitive and clear.
Things I'd want to experiment with if I was implementing this:
* A "wheel" effect where the items in the list grow slightly as they near the chosen item which stays locked in the interface at the center, popping into place at at each 'click'. Somewhat like the Price Is Right wheel flipper
* Making the interaction entirely scroll based once I click. Setting the item in place can be done by any other click or keypress, and cancelled with the escape hotkey. My interaction is pick, scroll, click (without having to aim back at the thing I just placed by scrolling)
"I've been tracking software quality metrics for three years" and then doesn't show any of the receipts, and simply lists anecdotal issues. I don't trust a single fact from this article.
My own anecdote: barely capable developers churning out webapps built on PHP and a poor understanding of Wordpress and jQuery were the norm in 2005. There's been an industry trend towards caring about the craft and writing decent code.
Most projects, even the messy ones I inherit from other teams today have Git, CI/CD, at least some tests, and a sane hosting infrastructure. They're also mosty built on decent platforms like Rails/Django/Next etc that impose some conventional structure. 20 years ago most of them were "SSH into the box and try not to break anything"
You'll identify on id.me
People have just gotten used to this sort of thing unfortunately
The tech just hasn’t been there yet and most of the devices that do this are heavy clunky and hot
Meta is investing billions to get out ahead of this shift and to own the entertainment and data (and thus advertising) layers that sit on top of the real world through these glasses
The rumor mill is abuzz that Facebook finally making a play for it in the next set of smart glasses after a few years of sticking to VR headsets and audio/camera only glasses
It’s remarkable, and I agree Claude 3.5 makes playing with local LLMs seem silly in comparison. Claude is useful for generating real work.
They want a 10x improvement from scaling and a 10x improvement from data and algorithmic changes
The sources of public data are essentially tapped
Algorithmic changes will be an unknown to us until they release, but from published research this remains a steady source of improvement
Scaling seems to stall if data is limited
So with all of that taken together, the logical step is to figure out how to turn compute into better data to train on. Enter strawberry / o1, and now o3
They can throw money, time, and compute at thinking about and then generating better training data. If the belief is that N billion new tokens of high quality training data will unlock the leap in capabilities they’re looking for, then it makes sense to delay the training until that dataset is ready
With o3 now public knowledge, imagine how long it’s been churning out new thinking at expert level across every field. OpenAI’s next moat may be the best synthetic training set ever.
At this point I would guess we get 4.5 with a subset of this - some scale improvement, the algorithmic pickups since 4 was trained, and a cleaned and improved core data set but without risking leakage of the superior dataset
When 5 launches, we get to see what a fully scaled version looks like with training data that outstrips average humans in almost every problem space
Then the next o-model gets to start with that as a base and reason? Its likely to be remarkable
There’s a daily 2.5 million token limit that you can use up fairly quickly with 100K context
So they may very well have completed the whole program with Claude. It’s just the machine literally stopped and the human had to do the final grunt work.
Aside from blindly copying and pasting a response, in which case the learner wasn’t interested in learning and probably would have plagiarized from somewhere else anyway, I have found LLM to be an incredible, endlessly patient teacher that I’m never afraid to ask a question of.
My kids who are in the tween and teenage years, are incredibly skeptical and dismissive of AI. They regard AI art as taking away creative initiative from artists and treat LLM similar to the way we treated Google growing up, if they use them at all. It’s a tool which can be helpful for answering questions that is part of the landscape of their knowledge building.
That knowledge acquisition includes school, YouTube and other short videos, their peers (online and off) Internet searches, and asking AI. Generally, I regard asking AI as one of the least problematic sources of info in that environment.
While I tend to be optimistic as a default, I truly do think that the ability to become less ignorant by asking questions is a net positive for humanity.
The only thing I truly lean on AI for right now is as an editor, helping me turn my detailed bullet points into decently crafted prose, and for generating clear and concise transcripts and takeaways from long meetings. To me that doesn’t seem like the downfall of human knowledge.
If they're not interested in getting paid to do that work now, it's a good signal for us that they won't be happy doing it when they're working with us. It's helped us find really wonderful people to work with.
OpenAI has a strong revenue model based on paid use
We don’t yet know the reason for that structure, but it appears across species and across different regions of the brain
It could be that a similarly modeled neuronal network would exhibit more pockets of specialized activity in response to certain inputs, and potentially even redundancies in specialization allowing it to process the inputs in slightly different ways to reach ensemble/consensus style outcomes. In some ways it sounds reminiscent of the mixture of experts approach
It’s certainly interesting!
This study is not about whether farmed or wild caught fish are better for you.
It’s the same reason I will always toggle the “include fees” on Airbnb’s search interface. Just tell me the real price.
Upfront pricing and an assumption I’ll get the item in 1-3 days is the real value prop, and I’m still a happy customer of that product
We have textual and visual models strong enough to look at a picture and describe it, or to read a body of text and categorize and label it well.
That work can then be used to feed the training of other models, whether it's larger models that can make use of an ever-growing well labelled corpus, or smaller and more efficient models trained on sets that were previously too niche or cost prohibitive to individually label.
With this coming out of Microsoft for copilot/Bing and the strength of recent smaller models which have had the benefit of GPT4 and other larger LLMs to assist with training, we appear to be at an inflection point of training quality at the same time as training compute is being massively scaled.
Both the capsule (alginate-based) and bacteria are common and safe