What I see: vibe coding is not dead: Non-eng teams inside of tech companies are just getting started with this approach, building on top of internal “harnesses for non eng teams” They are also learning limitations of “vibe coding” v quickly!
vibe coding is not dead: Non-eng teams inside of tech companies are just getting started with this approach, building on top of internal “harnesses for non eng teams” They are also learning limitations of “vibe coding” v quickly!
one way to understand a data center is that it's a device for burning natural gas, the latest excuse (after Ukraine and Iran) for the fuel that Big Oil is counting on to keep its business model afloat even as the world turns away from internal combustion cars and crude demand begins to drop.
Inside Anthropic and OpenAI, internal models are improving even faster. Over the summer there was a step change, as Mythos and Astra started kicking off the early stages of recursive self-improvement (RSI).
Most of starting a startup is the same. Most of starting a startup is always the same, right? In you know, microprocessors or AI or like internal combustion engine, it's always the same stuff.
It is highly fortunate that the OpenAI agents hacked HuggingFace. This is the only reason we know about all the severe internal failures at OpenAI, and gives us an opportunity to wake up before it is too late.
A less bad version of it is known to have happened, and from the outside it seems likely that worse things have happened internally that we never heard about.
And so speed determines success and failure and speed is determined by infrastructure. This is driven by really boring sounding things like how well do your purchasing and recruiting and spending processes work. This is as important as how well do you understand understand the object level technical content of the thing that you're building.
since 1970, natural forcings have been roughly flat, internal variability averages out over the longer period, and greenhouse gases explain essentially all of the observed warming.
Then you had code review which gave you another level of feedback. You could roll out internally more frequently. And everybody was using Facebook for all kinds of stuff, personal and internal business stuff. So whatever feature you developed, people would start using it immediately. So you get another round of feedback. Then we had this phased roll out process where you'd start rolling your stuff out. If there was a problem, the blast radius would be limited to a a few million people.
Generative AI has no internal, designed momentum towards truth and accuracy (beyond the absurdly diminishing returns of energy-hungry multi-layered LLMs), but a person or an institution can (and should).
You know, there's open source and there's open development. And and and we were technically open source in the beginning, but it was not open development. We would sort of lob the source code out in this repository and scrape the issues off of that and put it into our internal issue tracker.
I look at um the talent base and I think it is best for us to create opportunity for people to keep on growing with fresh new challenges within the company because if we don't do that they would leave the company
So you get this rapid, incredible great talent, rapid innovation because of open source and just, you know, the nature of friends, and, and insane competition. Among the company, what emerges is incredible stuff. And so this is the fastest innovating country in the world today
We argue that this profound adaptability is fundamentally linked to the efficient construction and refinement of internal representations of the environment, commonly referred to as world models, and we refer to this adaptation mechanism as world model induction.
is this idea that we form these internal models at particular points of high prediction error or points of, I believe also points of uncertainty, points of surprise or motivationally significant periods. And those points are when it’s maximally optimal to encode an episodic memory.
This raises public concerns that, at this late date, DoD might not have such a specification, whether classified or unclassified, even for its own internal use.
Instead of meetings, I used Workplace posts (Meta’s internal version of Facebook Group posts) to share thoughts, start discussions, and make announcements.
The biggest issue is probably that we don’t control neutral networks enough to be able to ensure AI doesn’t harm humans. We can’t even control AI to not reveal internal prompts.
Songs arise out of suffering, by which I mean they are predicated upon the complex, internal human struggle of creation and, well, as far as I know, algorithms don’t feel. Data doesn’t suffer.
Sparta was – if you will permit the comparison – an ancient North Korea. An over-militarized, paranoid state which was able only to protect its own systems of internal brutality and which added only oppression to the sum of the human experience.
Not only is Tina Fey a very funny writer, it's fascinating to have the opportunity to climb inside the head of a successful female leader. Reading the internal drama within Tina's head as she thought about whether she wanted a second child while considering how it would affect the 100+ employees that worked for her on 30 Rock was priceless. As a manager, it gives me an understanding and level of empathy I wouldn't have had otherwise.
A korrent is a belief a person has stated in their own words: one
sentence stating the claim, backed by a quote and a source, kept at
korrents.com.
Under a name here, the quoted block is what they actually said.
The korrent beneath it is the claim those words support, in
korrents' wording — tap it to see the record, its source, and who
else holds it.
Nobody here wrote their own korrents. They are compiled from public
statements, and a person can change their mind, which is recorded too.
About the English under a post
Some people here publish in a language other than English. Where they
do, this site shows a machine translation beneath the post, in
this typeface — the site's own, not theirs.
The post itself is never changed, moved or hidden: what is set in the
serif above is exactly what the person published, and it is what to
quote them on. A translation can be wrong in ways that matter,
especially about tone.
Only the post's own words are translated. A quoted post, a linked
article and a belief on korrents.com
are left in their original language.